Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Deaner, Ben"'
Autor:
Deaner, Ben, Ku, Hyejin
In economic program evaluation, it is common to obtain panel data in which outcomes are indicators that an individual has reached an absorbing state. For example, they may indicate whether an individual has exited a period of unemployment, passed an
Externí odkaz:
http://arxiv.org/abs/2405.05220
Autor:
Deaner, Ben
This thesis consists of three chapters. In each chapter I consider a particular problem in econometrics with implications for applied research, and in each case I attempt to solve that problem. In Chapter 1 I consider the task of inferring causal eff
Externí odkaz:
https://hdl.handle.net/1721.1/138934
Autor:
Deaner, Ben
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 tackle the problem of general measurement error. We call this the `trip
Externí odkaz:
http://arxiv.org/abs/2204.13815
Autor:
Deaner, Ben
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 confounders. One set of proxies are `negative control treatments' and
Externí odkaz:
http://arxiv.org/abs/2110.03973
Autor:
Deaner, Ben
Estimation and inference in dynamic discrete choice models often relies on approximation to lower the computational burden of dynamic programming. Unfortunately, the use of approximation can impart substantial bias in estimation and results in invali
Externí odkaz:
http://arxiv.org/abs/2010.11482
Autor:
Deaner, Ben
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 instruments are not quite valid, then NPIV estimates can be subject to
Externí odkaz:
http://arxiv.org/abs/1901.01241
Autor:
Deaner, Ben
We provide new results for nonparametric identification, estimation, and inference of causal effects using `proxy controls': observables that are noisy but informative proxies for unobserved confounding factors. Our analysis applies to cross-sectiona
Externí odkaz:
http://arxiv.org/abs/1810.00283