12 citations · 46 across the 19 of their papers we have counts for
20 papers
Algorithmic Feature Highlighting for Human-AI Decision-Making
Yifan Guo, Jann Spiess
Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate all available information. In s…
Causal Effect Estimation with Latent Textual Treatments
Omri Feldman, Amar Venugopal, Jann Spiess +1
Understanding the causal effects of text on downstream outcomes is a central task in many applications. Estimating such effects requires researchers to run controlled experiments t…
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…
Designing Algorithmic Recommendations to Achieve Human-AI Complementarity
Bryce McLaughlin, Jann Spiess
Algorithms frequently assist, rather than replace, human decision-makers. However, the design and analysis of algorithms often focus on predicting outcomes and do not explicitly mo…
Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests
Rahul Ladhania, Jann Spiess, Lyle Ungar +1
We consider learning personalized assignments to one of many treatment arms from a randomized controlled trial. Standard methods that estimate heterogeneous treatment effects separ…
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…
Algorithmic Assistance with Recommendation-Dependent Preferences
Bryce McLaughlin, Jann Spiess
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. However,…
From Quantum Codes to Gravity: A Journey of Gravitizing Quantum Mechanics
ChunJun Cao
In this note, I review a recent approach to quantum gravity that "gravitizes" quantum mechanics by emerging geometry and gravity from complex quantum states. Drawing further insigh…
On the Fairness of Machine-Assisted Human Decisions
Talia Gillis, Bryce McLaughlin, Jann Spiess
When machine-learning algorithms are used in high-stakes decisions, we want to ensure that their deployment leads to fair and equitable outcomes. This concern has motivated a fast-…
Unpacking the Black Box: Regulating Algorithmic Decisions
Laura Blattner, Scott Nelson, Jann Spiess
What should regulators of complex algorithms regulate? We propose a model of oversight over 'black-box' algorithms used in high-stakes applications such as lending, medical testing…
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…
Revisiting Event Study Designs: Robust and Efficient Estimation
Kirill Borusyak, Xavier Jaravel, Jann Spiess
We develop a framework for difference-in-differences designs with staggered treatment adoption and heterogeneous causal effects. We show that conventional regression-based estimato…
Finding Subgroups with Significant Treatment Effects
Jann Spiess, Vasilis Syrgkanis, Victor Yaneng Wang
Researchers often run resource-intensive randomized controlled trials (RCTs) to estimate the causal effects of interventions on outcomes of interest. Yet these outcomes are often n…
A Design-Based Perspective on Synthetic Control Methods
Lea Bottmer, Guido Imbens, Jann Spiess +1
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 observatio…
Bias Reduction in Instrumental Variable Estimation through First-Stage Shrinkage
Jann Spiess
The two-stage least-squares (2SLS) estimator is known to be biased when its first-stage fit is poor. I show that better first-stage prediction can alleviate this bias. In a two-sta…
Unbiased Shrinkage Estimation
Jann Spiess
Shrinkage estimation usually reduces variance at the cost of bias. But when we care only about some parameters of a model, I show that we can reduce variance without incurring bias…
Machine-Learning Tests for Effects on Multiple Outcomes
Jens Ludwig, Sendhil Mullainathan, Jann Spiess
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…