Search Results for author:
Found 13 papers, 2 papers with code
Machine-Learning Tests for Effects on Multiple Outcomes
In this paper we present tools for applied researchers that re-purpose off-the-shelf methods from the computer-science field of machine learning to create a "discovery engine" for data from randomized controlled trials (RCTs).
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.
Finding Subgroups with Significant Treatment Effects
In this paper, we propose a machine-learning method that is specifically optimized for finding such subgroups in noisy data.
Improving Inference from Simple Instruments through Compliance Estimation
In the case where both the treatment and instrument are binary and the instrument is independent of baseline covariates, we study weighting each observation according to its estimated compliance (that is, its conditional probability of being affected by the instrument), which we motivate from a (constrained) solution of the first-stage prediction problem implicit to IV.
Revisiting Event Study Designs: Robust and Efficient Estimation
We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects.
Unpacking the Black Box: Regulating Algorithmic Decisions
We show how to optimally regulate prediction algorithms in a world where an agent uses complex 'black-box' prediction functions to make decisions such as lending, medical testing, or hiring, and where a principal is limited in how much she can learn about the agent's black-box model.
On the Fairness of Machine-Assisted Human Decisions
In this article, we therefore consider in a formal model and in a lab experiment how properties of machine predictions affect the resulting human decisions.
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.
Algorithmic Assistance with Recommendation-Dependent Preferences
When an algorithm provides risk assessments, we typically think of them as helpful inputs to human decisions, such as when risk scores are presented to judges or doctors.
Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability
We show that implementation requires pre-analysis plans.
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.
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data.
Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests
We consider learning personalized assignments to one of many treatment arms from a randomized controlled trial.
