On Causal Inference with Model-Based Outcomes
We study a causal inference problem with group-level outcomes, which are themselves parameters identified from microdata. We formalize these outcomes using population moment conditions and demonstrate that one-step Generalized Method of Moments (GMM) estimators are generally inconsistent due to an endogenous weighting bias, where policy affects the implicit GMM weights. In contrast, two-stage Minimum Distance (MD) estimators perform well when group sizes are sufficiently large. While MD estimators can still be inconsistent in small groups due to a policy-induced sample selection, we demonstrate that this can be addressed by incorporating auxiliary population information. An empirical application illustrates the practical importance of these findings.
Code (1)
Tasks
Causal InferencemodelregressionSimilar Papers 제목 키워드 기반
Causal Inference Isn't Special: Why It's Just Another Prediction Problem
Causal inference is often portrayed as fundamentally distinct from predictive modeling, with its own terminology, goals, and intellectual challenges. But at its core, causal inference is simply a structured instance of p…
Causal InferenceDomain AdaptationDiffPO: A causal diffusion model for learning distributions of potential outcomes
Predicting potential outcomes of interventions from observational data is crucial for decision-making in medicine, but the task is challenging due to the fundamental problem of causal inference. Existing methods are larg…
Causal InferenceDecision MakingDenoisingSelection biasCausal Inference with Unstructured Outcomes
Causal inference has traditionally centered on scalar outcomes: whether a patient recovers, how much a worker earns, or how many visits a website receives. Modern studies increasingly ask causal questions about outcomes …
Causal InferenceCausal Inference on Outcomes Learned from Text
We propose a machine-learning tool that yields causal inference on text in randomized trials. Based on a simple econometric framework in which text may capture outcomes of interest, our procedure addresses three question…
Causal InferencevalidCausal Inference for Genomic Data with Multiple Heterogeneous Outcomes
With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the ind…
Causal Inference