Identification and estimation of treatment effects in a linear factor model with fixed number of time periods
This paper provides a new approach for identifying and estimating the Average Treatment Effect on the Treated under a linear factor model that allows for multiple time-varying unobservables. Unlike the majority of the literature on treatment effects in linear factor models, our approach does not require the number of pre-treatment periods to go to infinity to obtain a valid estimator. Our identification approach employs a certain nonlinear transformations of the time invariant observed covariates that are sufficiently correlated with the unobserved variables. This relevance condition can be checked with the available data on pre-treatment periods by validating the correlation of the transformed covariates and the pre-treatment outcomes. Based on our identification approach, we provide an asymptotically unbiased estimator of the effect of participating in the treatment when there is only one treated unit and the number of control units is large.
Code (0)
등록된 구현이 없습니다.
Tasks
validSimilar Papers 제목 키워드 기반
Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime
We propose a generalization of the synthetic control and synthetic interventions methodology to the dynamic treatment regime. We consider the estimation of unit-specific treatment effects from panel data collected via a …
Partial Identification of Treatment Effects with Implicit Generative Models
We consider the problem of partial identification, the estimation of bounds on the treatment effects from observational data. Although studied using discrete treatment variables or in specific causal graphs (e.g., instru…
SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification
Precise estimation of treatment effects is crucial for evaluating intervention effectiveness. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effec…
counterfactualDiversityIdentification and Estimation of Spillover Effects in Randomized Experiments
I study identification, estimation and inference for spillover effects in experiments where units' outcomes may depend on the treatment assignments of other units within a group. I show that the commonly-used reduced-for…
Doubly Robust Identification of Causal Effects of a Continuous Treatment using Discrete Instruments
Many empirical applications estimate causal effects of a continuous endogenous variable (treatment) using a binary instrument. Estimation is typically done through linear 2SLS. This approach requires a mean treatment cha…
valid