7 citations · 9 across the 7 of their papers we have counts for
7 papers
Linear Estimation of Structural and Causal Effects for Nonseparable Panel Data
Victor Chernozhukov, Ben Deaner, Ying Gao +2
This paper develops linear estimators for structural and causal parameters of nonseparable models using panel data. These models incorporate unobserved, time-varying, individual he…
Simple Specification-Free Causal Inference in Large Panels
Ben Deaner, Chen-Wei Hsiang, Andrei Zeleneev
We propose a simple and transparent approach to causal inference using panel data with a large number of units and periods. Instead of imposing a particular parametric specificatio…
Controlling for Latent Confounding with Triple Proxies
Ben Deaner
We present new results for nonparametric identification of causal effects using noisy proxies for unobserved confounders. Our approach builds on the results of \citet{Hu2008} who t…
Many Proxy Controls
Ben Deaner
A recent literature considers causal inference using noisy proxies for unobserved confounding factors. The proxies are divided into two sets that are independent conditional on the…
Approximation-Robust Inference in Dynamic Discrete Choice
Ben Deaner
Estimation and inference in dynamic discrete choice models often relies on approximation to lower the computational burden of dynamic programming. Unfortunately, the use of approxi…
Nonparametric Instrumental Variables Estimation Under Misspecification
Ben Deaner
Nonparametric Instrumental Variables (NPIV) analysis is based on a conditional moment restriction. We show that if this moment condition is even slightly misspecified, say because…
Proxy Controls and Panel Data
Ben Deaner
We provide new results for nonparametric identification, estimation, and inference of causal effects using `proxy controls': observables that are noisy but informative proxies for…