Trusting the Untestable: Validation and Diagnostics for the Doubly Robust Models

This post details the implementation and validation of Augmented Inverse Propensity Weighting (AIPW) models for quasi-experimental causal inference at Lyft. It introduces the concept of doubly robust estimation, the critical need for validation in non-randomized settings, and the platform's features for confounder management (pre-defined and customizable sets, leakage prevention) and model diagnostics (common support/propensity overlap, covariate balance). It also describes the scientific refinements for correcting downsampling bias through propensity score correction and outcome reweighting, and an empirical validation using weekly ride challenges to compare AIPW estimates with randomized experiment results.