The Labor Force Survey, a large-scale government statistics, and the causal forest algorithm are used to estimate the group average treatment effect of the COVID-19 on the employment status for each month from January to June 2020. We find that (1) because of the seasonality in employment status at monthly level, whether we use January 2020 as the base month for comparison, as done in most of the studies or whether we use the same month last year as the base comparison group makes a large difference; (2) whether we include those who are absent from work among the employed or not makes a large difference in the measure of the impact of COVID-19 and its changes; (3) if we use the employment measure which does not include those who are absent from work among the employed, 25–30% among the employed are adversely affected and that 10% of the employed experienced more than 10% decline in employment probability in April, 2020; (4) those who are the most affected by the COVID-19 are those who are unemployed or work part-time in the hotel and restaurant industry and service occupations; (5) in addition, younger and female respondents are more affected than are older and male respondents; and (6) we observe no clear differences in the impacts of COVID-19 with respect to living location, education status, and firm size among the most affected.

劳动力调查、大规模政府统计数据和因果森林算法用于估计COVID-19对2020年1月至2020年6月每个月的就业状况的群体平均治疗效果。我们发现（1）由于月度就业状况的季节性，我们是否像大多数研究那样使用 2020 年 1 月作为基准月进行比较，或者我们是否使用去年同月作为基准比较组会产生很大差异；(2) 我们是否将缺勤者包括在受雇者中对衡量 COVID-19 的影响及其变化有很大影响；(3) 如果我们使用的就业措施不包括被就业者中的缺勤者，2020 年 4 月，25-30% 的就业者受到不利影响，10% 的就业者就业概率下降超过 10%；(4) 受 COVID-19 影响最大的是那些失业或在酒店和餐饮业和服务业从事兼职工作的人；(5) 此外，年轻和女性受访者比年长和男性受访者受到的影响更大；(6) 我们观察到 COVID-19 对受影响最大的人在居住地点、教育状况和公司规模方面的影响没有明显差异。年轻和女性受访者比年长和男性受访者受到的影响更大；(6) 我们观察到 COVID-19 对受影响最大的人在居住地点、教育状况和公司规模方面的影响没有明显差异。年轻和女性受访者比年长和男性受访者受到的影响更大；(6) 我们观察到 COVID-19 对受影响最大的人在居住地点、教育状况和公司规模方面的影响没有明显差异。