Combining Forces: New Two-stage Approaches for Causal Inference

• Project/Area Number: 25K05032
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 07030:Economic statistics-related
• Research Institution: University of Tsukuba
• Principal Investigator: 余 征飛 筑波大学, 人文社会系, 准教授 (40774758)
• Project Period (FY): 2025-04-01 – 2029-03-31
• Project Status: Granted (Fiscal Year 2025)
• Budget Amount: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000); Fiscal Year 2028: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000); Fiscal Year 2027: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000); Fiscal Year 2026: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000); Fiscal Year 2025: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
• Keywords: Causal inference / Endogeneity / Bayesian method / Bootstrap

Outline of Research at the Start

This project investigates scenarios in which combining estimators of different natures helps to facilitate and enrich causal inference in economics. It focuses on two models. Model #1: for discrete choice models with endogeneity, this project develops a method that combines a first-stage frequentist estimator for the control function with a second-stage Bayesian approach for the structural parameters. Model #2: for the individual treatment effect (ITE) in a non-separable triangular model, this project studies pointwise and uniform inference of the distribution of ITE.