An exposure-first, three-stage penalized feature selection framework claims to keep confounders and outcome predictors, drop treatment predictors and noise, and reduce bias and variance in ATT estimation.
On a Class of Bias-Amplifying Variables that Endanger Effect Estimates
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abstract
This note deals with a class of variables that, if conditioned on, tends to amplify confounding bias in the analysis of causal effects. This class, independently discovered by Bhattacharya and Vogt (2007) and Wooldridge (2009), includes instrumental variables and variables that have greater influence on treatment selection than on the outcome. We offer a simple derivation and an intuitive explanation of this phenomenon and then extend the analysis to non linear models. We show that: 1. the bias-amplifying potential of instrumental variables extends over to non-linear models, though not as sweepingly as in linear models; 2. in non-linear models, conditioning on instrumental variables may introduce new bias where none existed before; 3. in both linear and non-linear models, instrumental variables have no effect on selection-induced bias.
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Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation
An exposure-first, three-stage penalized feature selection framework claims to keep confounders and outcome predictors, drop treatment predictors and noise, and reduce bias and variance in ATT estimation.