Search Results for author: Guido Imbens

Found 18 papers, 5 papers with code

Synthetic Difference In Differences Estimation

no code implementations27 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.

Long-term Causal Inference Under Persistent Confounding via Data Combination

no code implementations15 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.

Causal Inference

Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations

no code implementations9 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.

A Design-Based Perspective on Synthetic Control Methods

no code implementations23 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).

Bayesian Meta-Prior Learning Using Empirical Bayes

no code implementations4 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.

Combinatorial Optimization Management

Optimal Experimental Design for Staggered Rollouts

1 code implementation9 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.

Decision Making Experimental Design

Using Wasserstein Generative Adversial Networks for the Design of Monte Carlo Simulations

2 code implementations5 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.

Econometrics Methodology

Ensemble Methods for Causal Effects in Panel Data Settings

no code implementations24 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.

Matrix Completion regression

Machine Learning Methods Economists Should Know About

no code implementations24 Mar 2019 Susan Athey, Guido Imbens

We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics.

BIG-bench Machine Learning Causal Inference +2

Balanced Linear Contextual Bandits

no code implementations15 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.

Causal Inference Multi-Armed Bandits

Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption

no code implementations15 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.

Fixed Effects and the Generalized Mundlak Estimator

no code implementations5 Jul 2018 Dmitry Arkhangelsky, Guido Imbens

We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity.


Estimation Considerations in Contextual Bandits

no code implementations19 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.

Causal Inference Econometrics +1

Matrix Completion Methods for Causal Panel Data Models

2 code implementations27 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

Optimized Regression Discontinuity Designs

1 code implementation4 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.


Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index

no code implementations30 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.

Causal Inference

Recursive Partitioning for Heterogeneous Causal Effects

1 code implementation5 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.


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