Raaz Dwivedi: Integrating Double Robustness into Causal Latent Factor Models

- Speaker: Raaz Dwivedi (Cornell University)
- Discussant: James Robins (Harvard University)
- Title: Integrating Double Robustness into Causal Latent Factor Models
- Abstract: Latent factor models are widely utilized for causal inference in panel data, involving multiple measurements across various units. Popular inference methods include matrix completion for estimating the average treatment effect (ATE) and the nearest neighbor approach for individual treatment effects (ITE). However, these methods respectively underperform with non-low-rank outcomes or when faced with diverse units in the data. To tackle these challenges, we integrate double robustness principles with factor models, introducing estimators designed to be resilient against such issues. We present a doubly robust matrix completion strategy for ATE, capable of ensuring consistency despite unobserved confounding, either with low-rank outcome matrices or propensity matrices, and providing superior error/confidence intervals when both matrices are low-rank. Next, we propose a doubly robust nearest neighbor method for ITE, designed to achieve consistent estimates in the presence of either similar units or measurements, with improved error/confidence intervals when both conditions are met.