Bias-robust causal inference for panel data
Measuring treatment effects while accounting for guesswork in missing data
Economists often estimate what would have happened to people without a treatment by filling in missing data—but that guesswork can distort the final answer. This paper introduces a method that catches and corrects for this hidden error, reporting wider but more honest confidence intervals. The approach outperforms standard alternatives, especially when data is limited, keeping its accuracy promise even when the unobserved data patterns are partially misspecified.
Policy decisions about job training, tax credits, or health programs often rest on estimates from observational data where the counterfactual is guessed. Traditional methods claim narrow confidence intervals but deliver false certainty—the coverage is nearly zero when data is sparse. This method trades some precision for honesty: its stated margins actually contain the true answer, making it safer for policymakers to rely on.