12 citations · 29 across the 13 of their papers we have counts for
9 papers · 1 filter
Testing Monotonicity in a Finite Population
Jiafeng Chen, Jonathan Roth, Jann Spiess
We consider the extent to which we can learn from a completely randomized experiment whether all individuals have treatment effects that are weakly of the same sign, a condition we…
Causal Inference on Outcomes Learned from Text
Iman Modarressi, Jann Spiess, Amar Venugopal
We propose a machine-learning tool that yields causal inference on text in randomized trials. Based on a simple econometric framework in which text may capture outcomes of interest…
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
Susan Athey, Niall Keleher, Jann Spiess
In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the…
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control
Jann Spiess, Guido Imbens, Amar Venugopal
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…
Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability
Maximilian Kasy, Jann Spiess
What is the purpose of pre-analysis plans, and how should they be designed? We model the interaction between an agent who analyzes data and a principal who makes a decision based o…
Improving Inference from Simple Instruments through Compliance Estimation
Stephen Coussens, Jann Spiess
Instrumental variables (IV) regression is widely used to estimate causal treatment effects in settings where receipt of treatment is not fully random, but there exists an instrumen…