Search Results for author: Guido W. Imbens

Found 7 papers, 4 papers with code

Double-Robust Two-Way-Fixed-Effects Regression For Panel Data

1 code implementation29 Jul 2021 Dmitry Arkhangelsky, Guido W. Imbens, Lihua Lei, Xiaoman Luo

We propose a new estimator for the average causal effects of a binary treatment with panel data in settings with general treatment patterns.

regression Vocal Bursts Valence Prediction

Doubly Robust Identification for Causal Panel Data Models

no code implementations20 Sep 2019 Dmitry Arkhangelsky, Guido W. Imbens

We focus on a different, complementary approach to identification where assumptions are made about the connection between the treatment assignment and the unobserved confounders.

Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics

no code implementations16 Jul 2019 Guido W. Imbens

In this essay I discuss potential outcome and graphical approaches to causality, and their relevance for empirical work in economics.


Synthetic Difference in Differences

4 code implementations24 Dec 2018 Dmitry Arkhangelsky, Susan Athey, David A. Hirshberg, Guido W. Imbens, Stefan Wager

We present a new estimator for causal effects with panel data that builds on insights behind the widely used difference in differences and synthetic control methods.


Sampling-based vs. Design-based Uncertainty in Regression Analysis

no code implementations6 Jun 2017 Alberto Abadie, Susan Athey, Guido W. Imbens, Jeffrey M. Wooldridge

We derive standard errors that account for design-based uncertainty instead of, or in addition to, sampling-based uncertainty.

Statistics Theory Econometrics Statistics Theory

Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis

1 code implementation25 Oct 2016 Nikolay Doudchenko, Guido W. Imbens

In a seminal paper Abadie, Diamond, and Hainmueller [2010] (ADH), see also Abadie and Gardeazabal [2003], Abadie et al. [2014], develop the synthetic control procedure for estimating the effect of a treatment, in the presence of a single treated unit and a number of control units, with pre-treatment outcomes observed for all units.


Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions

1 code implementation25 Apr 2016 Susan Athey, Guido W. Imbens, Stefan Wager

There are many settings where researchers are interested in estimating average treatment effects and are willing to rely on the unconfoundedness assumption, which requires that the treatment assignment be as good as random conditional on pre-treatment variables.

Methodology Econometrics Statistics Theory Statistics Theory

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