Causal-Inference

We link Friedman's partial dependence plot with Pearl's backdoor adjustment formula. We discuss situations when possible causal interpretations can be made for black-box machine learning models.

This is a contributed discussion on the article 'Causal inference by using invariant prediction' by Peters et al.

This paper extends the dual interpretation of entropy balancing to general situations and proposes a tailored loss function. Minimizing this loss function by machine learning algorithms generates approximate covariate balance in large function …

Confounding introduces hidden bias to the statistical inference. We show in modern simultaneous testing, it is possible to correct for unmeasured confounders. Previous methods including SVA, LEAPP, RUV are unified in the same framework in this paper. …

We show a recently proposed method called Entropy Balancing is doubly robust, that is the causal effect estimator is consistent if the propensity score is logistic and/or the outcome regression model is linear in the covariates.