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Found 22 papers, 6 papers with code
Recursive Partitioning for Heterogeneous Causal Effects
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.
Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
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.
Optimized Regression Discontinuity Designs
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.
Methodology
Matrix Completion Methods for Causal Panel Data Models
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
Estimation Considerations in Contextual Bandits
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.
Fixed Effects and the Generalized Mundlak Estimator
We develop a new approach for estimating average treatment effects in observational studies with unobserved group-level heterogeneity.
Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption
In this paper we study estimation of and inference for average treatment effects in a setting with panel data.
Balanced Linear Contextual Bandits
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.
Ensemble Methods for Causal Effects in Panel Data Settings
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.
Machine Learning Methods Economists Should Know About
We discuss the relevance of the recent Machine Learning (ML) literature for economics and econometrics.
Using Wasserstein Generative Adversial Networks for the Design of Monte Carlo Simulations
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
Optimal Experimental Design for Staggered Rollouts
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.
Bayesian Meta-Prior Learning Using Empirical Bayes
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.
A Design-Based Perspective on Synthetic Control Methods
Since their introduction in Abadie and Gardeazabal (2003), Synthetic Control (SC) methods have quickly become one of the leading methods for estimating causal effects in observational studies in settings with panel data.
Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls
We investigate the optimal design of experimental studies that have pre-treatment outcome data available.
Covariate Balancing Sensitivity Analysis for Extrapolating Randomized Trials across Locations
The ability to generalize experimental results from randomized control trials (RCTs) across locations is crucial for informing policy decisions in targeted regions.
Long-term Causal Inference Under Persistent Confounding via Data Combination
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.
Synthetic Difference In Differences Estimation
In this paper, we describe a computational implementation of the Synthetic difference-in-differences (SDID) estimator of Arkhangelsky et al. (2021) for Stata.
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
Motivated by a recent literature on the double-descent phenomenon in machine learning, we consider highly over-parameterized models in causal inference, including synthetic control with many control units.
Estimating the Value of Evidence-Based Decision Making
Business/policy decisions are often based on evidence from randomized experiments and observational studies.
Causal clustering: design of cluster experiments under network interference
This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of network spillovers.
Causal Models for Longitudinal and Panel Data: A Survey
This recent literature has focused on credibly estimating causal effects of binary interventions in settings with longitudinal data, with an emphasis on practical advice for empirical researchers.
