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.

使用大规模政府统计数据“劳动力调查”和因果森林算法来估计 2020 年 1 月至 6 月每个月 COVID-19 对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，无论是像大多数研究那样以2020年1月为比较基准月，还是以去年同月作为比较基准月，都会有很大差异； (2) 我们是否将缺勤人员纳入就业人员范围，对于衡量 COVID-19 及其变化的影响有很大差异； （3）如果我们采用不包括就业者中缺勤的就业衡量标准，那么25-30%的就业者会受到不利影响，并且10%的就业者的就业概率会下降10%以上。 2020 年 4 月； (4) 受COVID-19影响最严重的是那些失业或在酒店、餐饮业和服务行业兼职的人； (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 对受影响最严重的人的居住地点、教育状况和公司规模的影响没有明显差异。