no code implementations • 27 Jan 2023 • Damian Clarke, Daniel Pailañir, Susan Athey, Guido Imbens
In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata.
no code implementations • 15 Feb 2022 • Guido Imbens, Nathan Kallus, Xiaojie Mao, Yuhao Wang
In this paper, we uniquely tackle the challenge of persistent unmeasured confounders, i. e., some unmeasured confounders that can simultaneously affect the treatment, short-term outcomes and the long-term outcome, noting that they invalidate identification strategies in previous literature.
no code implementations • 9 Dec 2021 • Xinkun Nie, Guido Imbens, Stefan Wager
The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions.
no code implementations • NeurIPS 2021 • Nick Doudchenko, Khashayar Khosravi, Jean Pouget-Abadie, Sebastien Lahaie, Miles Lubin, Vahab Mirrokni, Jann Spiess, Guido Imbens
We investigate the optimal design of experimental studies that have pre-treatment outcome data available.
no code implementations • 23 Jan 2021 • Lea Bottmer, Guido Imbens, Jann Spiess, Merrill Warnick
Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s).
no code implementations • 4 Feb 2020 • Sareh Nabi, Houssam Nassif, Joseph Hong, Hamed Mamani, Guido Imbens
Our Empirical Bayes method clamps features in each group together and uses the deployed model's observed data to empirically compute a hierarchical prior in hindsight.
1 code implementation • 9 Nov 2019 • Ruoxuan Xiong, Susan Athey, Mohsen Bayati, Guido Imbens
Next, we study an adaptive experimental design problem, where both the decision to continue the experiment and treatment assignment decisions are updated after each period's data is collected.
2 code implementations • 5 Sep 2019 • Susan Athey, Guido Imbens, Jonas Metzger, Evan Munro
We discuss the use of Wasserstein Generative Adversarial Networks (WGANs) as a method for systematically generating artificial data that mimic closely any given real data set without the researcher having many degrees of freedom.
no code implementations • 24 Mar 2019 • Susan Athey, Mohsen Bayati, Guido Imbens, Zhaonan Qu
This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment.
no code implementations • 24 Mar 2019 • Susan Athey, Guido Imbens
We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics.
no code implementations • 15 Dec 2018 • Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens
Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning.
no code implementations • 15 Aug 2018 • Susan Athey, Guido Imbens
In this paper we study estimation of and inference for average treatment effects in a setting with panel data.
no code implementations • 5 Jul 2018 • Dmitry Arkhangelsky, Guido Imbens
We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity.
no code implementations • 19 Nov 2017 • Maria Dimakopoulou, Zhengyuan Zhou, Susan Athey, Guido Imbens
We develop parametric and non-parametric contextual bandits that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias.
2 code implementations • 27 Oct 2017 • Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, Khashayar Khosravi
In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations.
Statistics Theory Econometrics Statistics Theory
1 code implementation • 4 May 2017 • Guido Imbens, Stefan Wager
The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting non-parametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest.
no code implementations • 30 Mar 2016 • Susan Athey, Raj Chetty, Guido Imbens, Hyunseung Kang
We focus primarily on a setting with two samples, an experimental sample containing data about the treatment indicator and the surrogates and an observational sample containing information about the surrogates and the primary outcome.
1 code implementation • 5 Apr 2015 • Susan Athey, Guido Imbens
The challenge is that the "ground truth" for a causal effect is not observed for any individual unit: we observe the unit with the treatment, or without the treatment, but not both at the same time.