Identification of Average Treatment Effects in Nonparametric Panel Models
It addresses identification challenges in causal inference for panel data, with potential applications like decomposing group-level differences such as the gender wage gap, but appears incremental as it builds on existing factor model frameworks.
The paper tackles the problem of identifying average treatment effects in panel data by introducing a novel nonparametric factor model and proving identification, with a result that enables consistent estimation of expected outcomes without treatment.
This paper studies identification of average treatment effects in a panel data setting. It introduces a novel nonparametric factor model and proves identification of average treatment effects. The identification proof is based on the introduction of a consistent estimator. Underlying the proof is a result that there is a consistent estimator for the expected outcome in the absence of the treatment for each unit and time period; this result can be applied more broadly, for example in problems of decompositions of group-level differences in outcomes, such as the much-studied gender wage gap.