TY - GEN
T1 - A robustness test for estimating total effects with covariate adjustment
AU - Su, Zehao
AU - Henckel, Leonard
PY - 2022
Y1 - 2022
N2 - Suppose we want to estimate a total effect with covariate adjustment in a linear structural equation model. We have a causal graph to decide what covariates to adjust for, but are uncertain about the graph. Here, we propose a testing procedure, that exploits the fact that there are multiple valid adjustment sets for the target total effect in the causal graph, to perform a robustness check on the graph. If the test rejects, it is a strong indication that we should not rely on the graph. We discuss what mistakes in the graph our testing procedure can detect and which ones it cannot and develop two strategies on how to select a list of valid adjustment sets for the procedure. We also connect our result to the related econometrics literature on coefficient stability tests
AB - Suppose we want to estimate a total effect with covariate adjustment in a linear structural equation model. We have a causal graph to decide what covariates to adjust for, but are uncertain about the graph. Here, we propose a testing procedure, that exploits the fact that there are multiple valid adjustment sets for the target total effect in the causal graph, to perform a robustness check on the graph. If the test rejects, it is a strong indication that we should not rely on the graph. We discuss what mistakes in the graph our testing procedure can detect and which ones it cannot and develop two strategies on how to select a list of valid adjustment sets for the procedure. We also connect our result to the related econometrics literature on coefficient stability tests
M3 - Article in proceedings
T3 - Proceedings of Machine Learning Research
SP - 1886
EP - 1895
BT - Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence
PB - PMLR
T2 - 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022)
Y2 - 1 August 2022 through 5 August 2022
ER -