  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 know which variables in the covariate set are
safe to adjust for and which are not. To address this problem, we develop
Nearly Invariant Causal Estimation (NICE). NICE uses invariant risk
minimization (IRM) [Arj19] to learn a representation of the covariates that,
under some assumptions, strips out bad controls but preserves sufficient
information to adjust for confounding. Adjusting for the learned
representation, rather than the covariates themselves, avoids the induced bias
and provides valid causal inferences. We evaluate NICE on both synthetic and
semi-synthetic data. When the covariates contain unknown collider variables and
other bad controls, NICE performs better than adjusting for all the covariates.

Machine Learning;Machine Learning