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Invariant Representation Learning for Treatment Effect Estimation
[article]
2021
arXiv
pre-print
The defining challenge for causal inference from observational data is the presence of 'confounders', covariates that affect both treatment assignment and the outcome. To address this challenge, practitioners collect and adjust for the covariates, hoping that they adequately correct for confounding. However, including every observed covariate in the adjustment runs the risk of including 'bad controls', variables that induce bias when they are conditioned on. The problem is that we do not always
arXiv:2011.12379v2
fatcat:5xa4etwyyrbwniiaxn5aehob2y