在未觀測干擾下利用短期結果識別與估計長期效果

Abstract

我們研究在未觀測干擾下，如何結合缺少長期結果的實驗樣本與缺少處理指派的觀察樣本，以識別長期處理效果︒當存在未觀測干擾時，標準的替代指數方法將無法使用︒我們利用未觀測干擾變數的代理變數，建立新的識別結果︒據此，我們發展雙重穩健的估計與推論程序︒我們將方法應用於 Job Corps 計畫，發現即使未觀測干擾使得標準替代指數估計產生偏誤，我們的方法仍能成功還原實驗基準︒

We study the identification of long-term treatment effects under unobserved confounding by combining an experimental sample, where the long-term outcome is missing, with an observational sample, where the treatment assignment is unobserved. While standard surrogate index methods fail when unobserved confounders exist, we establish novel identification results by leveraging proxy variables for the unobserved confounders. We further develop doubly robust estimation and inference procedures based on these results. Applying our method to the Job Corps program, we demonstrate its ability to recover experimental benchmarks even when unobserved confounders bias standard surrogate index estimates.