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Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population. J. Causal Inference (IF 1.4) Pub Date : 2022-12-09 Dasom Lee,Shu Yang,Xiaofei Wang
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples
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Decomposition of the total effect for two mediators: A natural mediated interaction effect framework. J. Causal Inference (IF 1.4) Pub Date : 2022-03-19 Xin Gao,Li Li,Li Luo
Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators. Decompositions of the total effect (TE) of an exposure variable into effects characterizing mediation pathways and interactions have gained an increasing amount of interest in the last decade
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The variance of causal effect estimators for binary v-structures J. Causal Inference (IF 1.4) Pub Date : 2022-01-01 Jack Kuipers,Giusi Moffa
Abstract Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally valid from a theoretical perspective, leading to identical causal effects. However, in practice, with finite data, estimators built on different
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Causal inference with imperfect instrumental variables J. Causal Inference (IF 1.4) Pub Date : 2022-01-01 Nikolai Miklin,Mariami Gachechiladze,George Moreno,Rafael Chaves
Abstract Instrumental variables allow for quantification of cause and effect relationships even in the absence of interventions. To achieve this, a number of causal assumptions must be met, the most important of which is the independence assumption, which states that the instrument and any confounding factor must be independent. However, if this independence condition is not met, can we still work
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A unifying causal framework for analyzing dataset shift-stable learning algorithms J. Causal Inference (IF 1.4) Pub Date : 2022-01-01 Adarsh Subbaswamy,Bryant Chen,Suchi Saria
Abstract Recent interest in the external validity of prediction models (i.e., the problem of different train and test distributions, known as dataset shift) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments. However, these methods consider different types of shifts and have been developed under
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Simple yet sharp sensitivity analysis for unmeasured confounding J. Causal Inference (IF 1.4) Pub Date : 2022-01-01 Jose M. Peña
Abstract We present a method for assessing the sensitivity of the true causal effect to unmeasured confounding. The method requires the analyst to set two intuitive parameters. Otherwise, the method is assumption free. The method returns an interval that contains the true causal effect and whose bounds are arbitrarily sharp, i.e., practically attainable. We show experimentally that our bounds can be
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Designing experiments informed by observational studies J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Evan T. R. Rosenman, Art B. Owen
The increasing availability of passively observed data has yielded a growing interest in “data fusion” methods, which involve merging data from observational and experimental sources to draw causal conclusions. Such methods often require a precarious tradeoff between the unknown bias in the observational dataset and the often-large variance in the experimental dataset. We propose an alternative approach
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Nonparametric inference for interventional effects with multiple mediators J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 David Benkeser, Jialu Ran
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estimators of these effects are based on parametric models with confidence interval estimation
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Novel bounds for causal effects based on sensitivity parameters on the risk difference scale J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Arvid Sjölander, Ola Hössjer
Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured
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Estimating causal effects with the neural autoregressive density estimator J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Sergio Garrido, Stanislav Borysov, Jeppe Rich, Francisco Pereira
The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables entailed by the graph conditional dependencies. In this article, we deviate from the common assumption of linear relationships
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On the bias of adjusting for a non-differentially mismeasured discrete confounder J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Jose M. Peña, Sourabh Balgi, Arvid Sjölander, Erin E. Gabriel
Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that adjustment for the mismeasured observed variables provides a less biased estimate of the true average causal effect than not adjusting. However, this
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Learning linear non-Gaussian graphical models with multidirected edges J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Yiheng Liu, Elina Robeva, Huanqing Wang
In this article, we propose a new method to learn the underlying acyclic mixed graph of a linear non-Gaussian structural equation model with given observational data. We build on an algorithm proposed by Wang and Drton, and we show that one can augment the hidden variable structure of the recovered model by learning multidirected edges rather than only directed and bidirected ones. Multidirected edges
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Conditional as-if analyses in randomized experiments J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Nicole E. Pashley, Guillaume W. Basse, Luke W. Miratrix
The injunction to “analyze the way you randomize” is well known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the merits of randomization-based inference seldom follow this injunction to the letter. Bernoulli randomized experiments are often analyzed as completely randomized experiments, and completely randomized experiments are analyzed
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Causal versions of maximum entropy and principle of insufficient reason J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Dominik Janzing
The principle of insufficient reason (PIR) assigns equal probabilities to each alternative of a random experiment whenever there is no reason to prefer one over the other. The maximum entropy principle (MaxEnt) generalizes PIR to the case where statistical information like expectations are given. It is known that both principles result in paradoxical probability updates for joint distributions of cause
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Incremental intervention effects in studies with dropout and many timepoints# J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Kwangho Kim, Edward H. Kennedy, Ashley I. Naimi
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set treatment values deterministically) to accommodate multiple outcomes and subject dropout
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Optimal balancing of time-dependent confounders for marginal structural models J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Nathan Kallus, Michele Santacatterina
Marginal structural models (MSMs) can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment weighting (IPTW) can be sensitive to model misspecification and lead to high-variance estimates due to extreme weights. Various methods have been proposed
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Estimating the effect of central bank independence on inflation using longitudinal targeted maximum likelihood estimation J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Philipp F. M. Baumann, Michael Schomaker, Enzo Rossi
The notion that an independent central bank reduces a country’s inflation is a controversial hypothesis. To date, it has not been possible to satisfactorily answer this question because the complex macroeconomic structure that gives rise to the data has not been adequately incorporated into statistical analyses. We develop a causal model that summarizes the economic process of inflation. Based on this
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A fundamental measure of treatment effect heterogeneity J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Jonathan Levy, Mark van der Laan, Alan Hubbard, Romain Pirracchio
The stratum-specific treatment effect function is a random variable giving the average treatment effect (ATE) for a randomly drawn stratum of potential confounders a clinician may use to assign treatment. In addition to the ATE, the variance of the stratum-specific treatment effect function is fundamental in determining the heterogeneity of treatment effect values. We offer a non-parametric plug-in
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Radical empiricism and machine learning research J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Judea Pearl
I contrast the “data fitting” vs “data interpreting” approaches to data science along three dimensions: Expediency, Transparency, and Explainability. “Data fitting” is driven by the faith that the secret to rational decisions lies in the data itself. In contrast, the data-interpreting school views data, not as a sole source of knowledge but as an auxiliary means for interpreting reality, and “reality”
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Decision-theoretic foundations for statistical causality J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Philip Dawid
We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions. DT reframes causal inference as “assisted decision-making” and aims to understand when, and how, I can make use of external data, typically observational, to help me solve a decision problem by taking
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Identification of causal intervention effects under contagion J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Xiaoxuan Cai, Wen Wei Loh, Forrest W. Crawford
Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment – such as a vaccine – given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped
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Two seemingly paradoxical results in linear models: the variance inflation factor and the analysis of covariance J. Causal Inference (IF 1.4) Pub Date : 2021-01-01 Peng Ding
A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease when including additional covariates into the regression. The variance inflation factor (VIF) measures the increase of the variance. Another result from a standard linear model or experimental design course is that including additional covariates in a linear
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Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness J. Causal Inference (IF 1.4) Pub Date : 2020-12-31 Nathan Corder, Shu Yang
Abstract The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing
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A note on a sensitivity analysis for unmeasured confounding, and the related E-value J. Causal Inference (IF 1.4) Pub Date : 2020-12-31 Arvid Sjölander
Abstract Unmeasured confounding is one of the most important threats to the validity of observational studies. In this paper we scrutinize a recently proposed sensitivity analysis for unmeasured confounding. The analysis requires specification of two parameters, loosely defined as the maximal strength of association that an unmeasured confounder may have with the exposure and with the outcome, respectively
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Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs J. Causal Inference (IF 1.4) Pub Date : 2020-12-23 Cristian Crespo
Abstract This paper elaborates on administrative sorting, a threat to internal validity that has been overlooked in the regression discontinuity (RD) literature. Variation in treatment assignment near the threshold may still not be as good as random even when individuals are unable to precisely manipulate the running variable. This can be the case when administrative procedures, beyond individuals’
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When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method J. Causal Inference (IF 1.4) Pub Date : 2020-12-19 Layla Parast, Priscillia Hunt, Beth Ann Griffin, David Powell
Abstract In some applications, researchers using the synthetic control method (SCM) to evaluate the effect of a policy may struggle to determine whether they have identified a “good match” between the control group and treated group. In this paper, we demonstrate the utility of the mean and maximum Absolute Standardized Mean Difference (ASMD) as a test of balance between a synthetic control unit and
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Instruments with Heterogeneous Effects: Bias, Monotonicity, and Localness J. Causal Inference (IF 1.4) Pub Date : 2020-12-19 Nick Huntington-Klein
Abstract In Instrumental Variables (IV) estimation, the effect of an instrument on an endogenous variable may vary across the sample. In this case, IV produces a local average treatment effect (LATE), and if monotonicity does not hold, then no effect of interest is identified. In this paper, I calculate the weighted average of treatment effects that is identified under general first-stage effect heterogeneity
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On the Monotonicity of a Nondifferentially Mismeasured Binary Confounder J. Causal Inference (IF 1.4) Pub Date : 2020-11-28 Jose M. Peña
Abstract Suppose that we are interested in the average causal effect of a binary treatment on an outcome when this relationship is confounded by a binary confounder. Suppose that the confounder is unobserved but a nondifferential proxy of it is observed. We show that, under certain monotonicity assumption that is empirically verifiable, adjusting for the proxy produces a measure of the effect that
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A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance J. Causal Inference (IF 1.4) Pub Date : 2020-11-28 Soojin Park, Esra Kürüm
Abstract Estimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation effects in the presence of treatment noncompliance. Existing studies based on the IV-based method focus on identifying the mediated portion
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Estimating population average treatment effects from experiments with noncompliance J. Causal Inference (IF 1.4) Pub Date : 2020-10-23 Kellie N. Ottoboni, Jason V. Poulos
Abstract Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We propose a reweighting method for estimating population average treatment effects in settings with noncompliance. Simulations show the proposed compliance-adjusted population estimator outperforms its unadjusted counterpart
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The Inflation Technique Completely Solves the Causal Compatibility Problem J. Causal Inference (IF 1.4) Pub Date : 2020-09-03 Miguel Navascués, Elie Wolfe
Abstract The causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible
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Post-randomization Biomarker Effect Modification Analysis in an HIV Vaccine Clinical Trial J. Causal Inference (IF 1.4) Pub Date : 2020-07-25 Peter B. Gilbert, Bryan S. Blette, Bryan E. Shepherd, Michael G. Hudgens
Abstract While the HVTN 505 trial showed no overall efficacy of the tested vaccine to prevent HIV infection over placebo, markers measuring immune response to vaccination were strongly correlated with infection. This finding generated the hypothesis that some marker-defined vaccinated subgroups were partially protected whereas others had their risk increased. This hypothesis can be assessed using the
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A Combinatorial Solution to Causal Compatibility J. Causal Inference (IF 1.4) Pub Date : 2020-07-25 Thomas C. Fraser
Abstract Within the field of causal inference, it is desirable to learn the structure of causal relationships holding between a system of variables from the correlations that these variables exhibit; a sub-problem of which is to certify whether or not a given causal hypothesis is compatible with the observed correlations. A particularly challenging setting for assessing causal compatibility is in the
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Identification and Estimation of Intensive Margin Effects by Difference-in-Difference Methods J. Causal Inference (IF 1.4) Pub Date : 2020-01-01 Markus Hersche, Elias Moor
This paper discusses identification and estimation of causal intensive margin effects. The causal intensive margin effect is defined as the treatment effect on the outcome of individuals with a positive outcome irrespective of whether they are treated or not, and is of interest for outcomes with corner solutions. The main issue is to deal with a potential selection problem that arises when conditioning
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Direct Effects under Differential Misclassification in Outcomes, Exposures, and Mediators J. Causal Inference (IF 1.4) Pub Date : 2020-01-01 Yige Li, Tyler J. VanderWeele
Direct effects in mediation analysis quantify the effect of an exposure on an outcome not mediated by a certain intermediate. When estimating direct effects through measured data, misclassification may occur in the outcomes, exposures, and mediators. In mediation analysis, any such misclassification may lead to biased estimates in the direct effects. Basing on the conditional dependence between the
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Improved Doubly Robust Estimation in Marginal Mean Models for Dynamic Regimes J. Causal Inference (IF 1.4) Pub Date : 2020-01-01 Hao Sun, Ashkan Ertefaie, Xin Lu, Brent A. Johnson
Doubly robust (DR) estimators are an important class of statistics derived from a theory of semiparametric efficiency. They have become a popular tool in causal inference, including applications to dynamic treatment regimes. The doubly robust estimators for the mean response to a dynamic treatment regime may be conceived through the augmented inverse probability weighted (AIPW) estimating function
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Averaging causal estimators in high dimensions J. Causal Inference (IF 1.4) Pub Date : 2020-01-01 Joseph Antonelli, Matthew Cefalu
Abstract There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one could use in this setting. Each of these estimators has different operating characteristics, and it is unlikely that one estimator will outperform
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Unifying Gaussian LWF and AMP Chain Graphs to Model Interference J. Causal Inference (IF 1.4) Pub Date : 2019-11-05 Jose M. Peña
Abstract An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can represent both interference and non-interference relationships for Gaussian
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A Kernel-Based Metric for Balance Assessment. J. Causal Inference (IF 1.4) Pub Date : 2018-12-01 Yeying Zhu,Jennifer S Savage,Debashis Ghosh
An important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in different treatment groups. We also introduce a new balance measure called kernel distance, which is the empirical estimate of the probability metric
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Identification of the joint effect of a dynamic treatment intervention and a stochastic monitoring intervention under the no direct effect assumption. J. Causal Inference (IF 1.4) Pub Date : 2017-12-15 Romain Neugebauer,Julie A Schmittdiel,Alyce S Adams,Richard W Grant,Mark J van der Laan
The management of chronic conditions is characterized by frequent re-assessment of therapy decisions in response to the patient's changing condition over the course of the illness. Evidence most suitable to inform care thus often concerns the contrast of adaptive treatment strategies that repeatedly personalize treatment decisions over time using the latest accumulated data available from the patient's
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Longitudinal Mediation Analysis with Time-varying Mediators and Exposures, with Application to Survival Outcomes. J. Causal Inference (IF 1.4) Pub Date : 2017-06-23 Wenjing Zheng,Mark van der Laan
In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes. At a given time point, the exposure and mediator of interest are influenced by past covariates, mediators and exposures, and affect future covariates, mediators and exposures. Right censoring, if present, occurs in response
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Chasing balance and other recommendations for improving nonparametric propensity score models. J. Causal Inference (IF 1.4) Pub Date : 2017-01-01 B A Griffin,D McCaffrey,D Almirall,C Setodji,L Burgette
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Semi-Parametric Estimation and Inference for the Mean Outcome of the Single Time-Point Intervention in a Causally Connected Population. J. Causal Inference (IF 1.4) Pub Date : 2016-11-29 Oleg Sofrygin,Mark J van der Laan
We study the framework for semi-parametric estimation and statistical inference for the sample average treatment-specific mean effects in observational settings where data are collected on a single network of connected units (e.g., in the presence of interference or spillover). Despite recent advances, many of the current statistical methods rely on estimation techniques that assume a particular parametric
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The Mechanics of Omitted Variable Bias: Bias Amplification and Cancellation of Offsetting Biases. J. Causal Inference (IF 1.4) Pub Date : 2016-11-08 Peter M Steiner,Yongnam Kim
Causal inference with observational data frequently requires researchers to estimate treatment effects conditional on a set of observed covariates, hoping that they remove or at least reduce the confounding bias. Using a simple linear (regression) setting with two confounders - one observed (X), the other unobserved (U) - we demonstrate that conditioning on the observed confounder X does not necessarily
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Markov Boundary Discovery with Ridge Regularized Linear Models. J. Causal Inference (IF 1.4) Pub Date : 2016-05-14 Eric V Strobl,Shyam Visweswaran
Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association to a response. However, many investigators are reluctant to draw causal interpretations of the selected variables due to the incomplete knowledge of
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A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments. J. Causal Inference (IF 1.4) Pub Date : 2016-02-16 Yeying Zhu,Donna L Coffman,Debashis Ghosh
In this article, we study the causal inference problem with a continuous treatment variable using propensity score-based methods. For a continuous treatment, the generalized propensity score is defined as the conditional density of the treatment-level given covariates (confounders). The dose-response function is then estimated by inverse probability weighting, where the weights are calculated from
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Surrogate Endpoint Evaluation: Principal Stratification Criteria and the Prentice Definition. J. Causal Inference (IF 1.4) Pub Date : 2016-01-02 Peter B Gilbert,Erin E Gabriel,Ying Huang,Ivan S F Chan
A common problem of interest within a randomized clinical trial is the evaluation of an inexpensive response endpoint as a valid surrogate endpoint for a clinical endpoint, where a chief purpose of a valid surrogate is to provide a way to make correct inferences on clinical treatment effects in future studies without needing to collect the clinical endpoint data. Within the principal stratification
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Sufficient Causes: On Oxygen, Matches, and Fires J. Causal Inference (IF 1.4) Pub Date : 2019-09-01 Judea Pearl
Abstract We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufficient cause of another, and how counterfactuals emerge organically from basic scientific knowledge, rather than manipulative experiments. We contrast this demonstration with the potential outcome framework and address the distinction between causes and enablers.
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A Gaussian Process Framework for Overlap and Causal Effect Estimation with High-Dimensional Covariates J. Causal Inference (IF 1.4) Pub Date : 2019-07-18 Debashis Ghosh, Efrén Cruz Cortés
Abstract A powerful tool for the analysis of nonrandomized observational studies has been the potential outcomes model. Utilization of this framework allows analysts to estimate average treatment effects. This article considers the situation in which high-dimensional covariates are present and revisits the standard assumptions made in causal inference. We show that by employing a flexible Gaussian
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The Inflation Technique for Causal Inference with Latent Variables J. Causal Inference (IF 1.4) Pub Date : 2019-07-16 Elie Wolfe, Robert W. Spekkens, Tobias Fritz
Abstract The problem of causal inference is to determine if a given probability distribution on observed variables is compatible with some causal structure. The difficult case is when the causal structure includes latent variables. We here introduce the inflation technique for tackling this problem. An inflation of a causal structure is a new causal structure that can contain multiple copies of each
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Regression Adjustments for Estimating the Global Treatment Effect in Experiments with Interference J. Causal Inference (IF 1.4) Pub Date : 2019-05-17 Alex Chin
Abstract Standard estimators of the global average treatment effect can be biased in the presence of interference. This paper proposes regression adjustment estimators for removing bias due to interference in Bernoulli randomized experiments. We use a fitted model to predict the counterfactual outcomes of global control and global treatment. Our work differs from standard regression adjustments in
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Estimating Causal Effects of New Treatments Despite Self-Selection: The Case of Experimental Medical Treatments J. Causal Inference (IF 1.4) Pub Date : 2019-04-26 Chad Hazlett
Abstract Providing terminally ill patients with access to experimental treatments, as allowed by recent “right to try” laws and “expanded access” programs, poses a variety of ethical questions. While practitioners and investigators may assume it is impossible to learn the effects of these treatment without randomized trials, this paper describes a simple tool to estimate the effects of these experimental
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Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction J. Causal Inference (IF 1.4) Pub Date : 2019-04-26 Wei Luo, Wenbo Wu, Yeying Zhu
Abstract Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional linear combination of the covariates that is sufficient to model the regression causal effect. Compared with the existing applications of
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Estimating Mann–Whitney-Type Causal Effects for Right-Censored Survival Outcomes J. Causal Inference (IF 1.4) Pub Date : 2019-04-26 Zhiwei Zhang, Chunling Liu, Shujie Ma, Min Zhang
Abstract Mann–Whitney-type causal effects are clinically relevant, easy to interpret, and readily applicable to a wide range of study settings. This article considers estimation of such effects when the outcome variable is a survival time subject to right censoring. We derive and discuss several methods: an outcome regression method based on a regression model for the survival outcome, an inverse probability
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New Traffic Conflict Measure Based on a Potential Outcome Model J. Causal Inference (IF 1.4) Pub Date : 2019-04-26 Kentaro Yamada, Manabu Kuroki
Abstract A key issue in the analysis of traffic accidents is to quantify the effectiveness of a given evasive action taken by a driver to avoid crashing. Since 1977, the widely accepted definition for this effectiveness measure, which is called traffic conflict, has been “the risk of a collision if the driver movement remains unchanged.” Although the definition is expressed counterfactually, the full
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Technical Considerations in the Use of the E-Value J. Causal Inference (IF 1.4) Pub Date : 2019-04-19 Tyler J. VanderWeele, Peng Ding, Maya Mathur
Abstract The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would have to have with both the exposure and the outcome, conditional on the measured covariates, to explain away the observed exposure-outcome association. We have elsewhere proposed that the reporting of E-values for estimates and for the limit of the confidence interval closest
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A Falsifiability Characterization of Double Robustness Through Logical Operators J. Causal Inference (IF 1.4) Pub Date : 2019-03-01 Constantine Frangakis
Abstract We address the characterization of problems in which a consistent estimator exists in a union of two models, also termed as a doubly robust estimator. Such estimators are important in missing information, including causal inference problems. Existing characterizations, based on the semiparametric theory of projections, have seen sufficient progress, but can still leave one’s understanding
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The Entry of Randomized Assignment into the Social Sciences J. Causal Inference (IF 1.4) Pub Date : 2019-03-01 Julian C. Jamison
Abstract Although the concept of randomized assignment in order to control for extraneous confounding factors reaches back hundreds of years, the first empirical use appears to have been in an 1835 trial of homeopathic medicine. Throughout the 19th century there was a growing awareness of the need for comparison groups, albeit often without the realization that randomization could be a clean method
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On the Interpretation of d o ( x )do(x) J. Causal Inference (IF 1.4) Pub Date : 2019-02-28 Judea Pearl
Abstract This paper provides empirical interpretation of the d o ( x )do(x) operator when applied to non-manipulable variables such as race, obesity, or cholesterol level. We view d o ( x )do(x) as an ideal intervention that provides valuable information on the effects of manipulable variables and is thus empirically testable. We draw parallels between this interpretation and ways of enabling machines
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Randomization Tests that Condition on Non-Categorical Covariate Balance J. Causal Inference (IF 1.4) Pub Date : 2019-01-18 Zach Branson, Luke W. Miratrix
Abstract A benefit of randomized experiments is that covariate distributions of treatment and control groups are balanced on average, resulting in simple unbiased estimators for treatment effects. However, it is possible that a particular randomization yields covariate imbalances that researchers want to address in the analysis stage through adjustment or other methods. Here we present a randomization