Causal Inference Using Factor Models

2026-06-28

AI Summary

This study addresses causal inference in panel data under policy interventions by moving beyond the conventional parallel trends assumption. It proposes a novel framework grounded in factor models, wherein treatment effects arise from structural changes in the exposure of treated units to latent common shocks—and can further accommodate shifts in the factor process itself. The approach is applicable to settings with either a single or multiple treated units and remains effective even when unit- and time-specific effects are not separately identifiable. By incorporating treatment-dependent factor structures and combining fixed-effect estimation with tailored inference strategies, the method yields confidence intervals whose empirical coverage closely matches nominal levels in simulations. Empirical applications to California’s tobacco control program and German reunification produce results consistent with synthetic control estimates while, for the first time, enabling formal statistical inference.

Abstract

We develop a factor-model framework for causal inference in panels with policy interventions. Treatment effects are represented as structural changes in treated units' exposure to latent common shocks and, in extensions, changes in the factor process itself. The approach does not impose the standard parallel-trends restriction, accommodates one or many treated units, and targets systematic effects when unit-time idiosyncratic effects are not point identified. We provide estimation and inference under both fixed and treatment-dependent factor processes. Simulations show coverage close to nominal levels. In applications to California tobacco control and German reunification, the method produces estimates broadly consistent with synthetic control while delivering formal confidence intervals.

Problem

• causal inference
• factor models
• policy interventions
• treatment effects
• panel data

Innovation

• factor models
• causal inference
• policy interventions
• structural change
• synthetic control