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Found 10 papers, 1 papers with code
Rationalizing Pre-Analysis Plans: Statistical Decisions Subject to Implementability
Pre-analysis plans (PAPs) are a potential remedy to the publication of spurious findings in empirical research, but they have been criticized for their costs and for preventing valid discoveries.
Algorithmic Assistance with Recommendation-Dependent Preferences
But when a decision-maker obtains a recommendation, they may not only react to the information.
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.
On the Fairness of Machine-Assisted Human Decisions
We show in a formal model that the inclusion of a biased human decision-maker can revert common relationships between the structure of the algorithm and the qualities of resulting decisions.
Unpacking the Black Box: Regulating Algorithmic Decisions
We characterize optimal oversight of algorithms in a world where an agent designs a complex prediction function but a principal is limited in the amount of information she can learn about the prediction function.
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.
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.
Evidence-Based Policy Learning
The past years have seen seen the development and deployment of machine-learning algorithms to estimate personalized treatment-assignment policies from randomized controlled trials.
A Design-Based Perspective on Synthetic Control Methods
Here we study SC methods from a design-based perspective, assuming a model for the selection of the treated unit(s) and period(s).
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).
