71 citations · 148 across the 11 of their papers we have counts for
12 papers
Doubly-robust causal inference for multi-valued treatment regimes with recurrent neural networks
Jason Poulos
We propose a doubly-robust method to estimate the causal effects of multi-valued treatment regimes on patient outcomes. A multi-valued treatment regime assigns personalized treatme…
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
Mike A. Merrill, Alexander G. Shaw, Nicholas Carlini +82
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not…
Gender gaps in frontier entrepreneurship? Evidence from 1901 Oklahoma land lottery winners
Jason Poulos
The paper investigates gender differences in entrepreneurship by exploiting a large-scale land lottery in Oklahoma at the turn of the 20 century. Lottery winners clai…
Retrospective causal inference via matrix completion, with an evaluation of the effect of European integration on cross-border employment
Jason Poulos, Andrea Albanese, Andrea Mercatanti +1
We propose a method of retrospective counterfactual imputation in panel data settings with later-treated and always-treated units, but no never-treated units. We use the observed o…
Amnesty Policy and Elite Persistence in the Postbellum South: Evidence from a Regression Discontinuity Design
Jason Poulos
This paper investigates the impact of Reconstruction-era amnesty policy on the officeholding and wealth of elites in the postbellum South. Amnesty policy restricted the political a…
Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison
Zhenhua Wang, Olanrewaju Akande, Jason Poulos +1
Multiple imputation (MI) is a popular approach for dealing with missing data arising from non-response in sample surveys. Multiple imputation by chained equations (MICE) is one of…
Adversarial Machine Learning: Bayesian Perspectives
David Rios Insua, Roi Naveiro, Victor Gallego +1
Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats: in certain scenarios there may be advers…
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction
Jason Poulos
This paper examines how homestead policies, which opened vast frontier lands for settlement, influenced the development of American frontier states. It uses a treatment propensity-…
Estimating population average treatment effects from experiments with noncompliance
Kellie Ottoboni, Jason Poulos
Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest. We pro…
Character-Based Handwritten Text Transcription with Attention Networks
Jason Poulos, Rafael Valle
The paper approaches the task of handwritten text recognition (HTR) with attentional encoder-decoder networks trained on sequences of characters, rather than words. We experiment o…
RNN-based counterfactual prediction, with an application to homestead policy and public schooling
Jason Poulos, Shuxi Zeng
This paper proposes a method for estimating the effect of a policy intervention on an outcome over time. We train recurrent neural networks (RNNs) on the history of control unit ou…
Missing Data Imputation for Supervised Learning
Jason Poulos, Rafael Valle
Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing mis…