18 citations · 31 across the 14 of their papers we have counts for
16 papers
Large-scale linear hypothesis testing for high-dimensional online M-estimation
Daeyoung Ham, Myeonghun Yu, Tate Jacobson
With the growing prevalence of streaming data, renewable estimation has become an important tool for updating statistical analyses without retaining historical raw observations. We…
Human-AI-Powered Hypothesis Testing: Cost-Aware Selective AI Scoring and Sequential Human Escalation
Dae Woong Ham, Dae Woong, Ham +3
Large language models are increasingly used as inexpensive judges to evaluate outputs, label data, and assess whether a system meets a desired quality standard. Yet using AI judgme…
Debiased inference for proximal dose-response function
Daeyoung Ham, Sihan Wu, Yifan Cui
We study nonparametric inference for a continuous-treatment causal dose-response curve and its first derivative under unmeasured confounding, using treatment- and outcome-inducing…
A General Framework for Optimal Group Sequential Testing via Mixed-Integer Linear Programming
Dae Woong Ham, Stefanus Jasin, Xuejun Zhao
Sequential hypothesis tests are widely adopted as a principled way to perform multiple tests on data that arrives over time. In particular, researchers frequently utilize group seq…
Benefits and Costs of Adaptive Sampling
Yu-Shiou Willy Lin, Dae Woong Ham, Iavor Bojinov
Multi-armed bandits are widely used for sequential experimentation in clinical trials, recommendation systems, and online platforms. While regret minimization and valid inference f…
Bias-Variance Tradeoff of Matching Prior to Difference-in-Differences When Parallel Trends is Violated
Mingxuan Ge, Dae Woong Ham
Quasi-experimental causal inference methods have become central in empirical operations management for guiding managerial decisions. Among these, empiricists utilize the Difference…
Sparse Multivariate Linear Regression with Strongly Associated Response Variables
Daeyoung Ham, Bradley S. Price, Adam J. Rothman
We propose new methods for multivariate linear regression when the regression coefficient matrix is sparse and the error covariance matrix is dense. We assume that the error covari…
Re-evaluating the impact of reduced malaria prevalence on birthweight in sub-Saharan Africa: A pair-of-pairs study via two-stage bipartite and non-bipartite matching
Pengyun Wang, Ping Huang, Yifan Jin +5
According to the WHO, in 2021, about 32% of pregnant women in sub-Saharan Africa were infected with malaria during pregnancy. Malaria infection during pregnancy can cause various a…
Doubly robust estimation and inference for a log-concave counterfactual density
Daeyoung Ham, Ted Westling, Charles R. Doss
We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context, we stud…
Fitted value shrinkage
Daeyoung Ham, Adam J. Rothman
We propose a penalized least-squares method to fit the linear regression model with fitted values that are invariant to invertible linear transformations of the design matrix. This…
Design-Based Inference for Multi-arm Bandits
Dae Woong Ham, Iavor Bojinov, Michael Lindon +1
Multi-arm bandits are gaining popularity as they enable real-world sequential decision-making across application areas, including clinical trials, recommender systems, and online d…
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Panel Experiments
Dae Woong Ham, Iavor Bojinov, Michael Lindon +1
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. Beyond aiding managerial decision-making, experiments…
Anytime-Valid Linear Models and Regression Adjusted Causal Inference in Randomized Experiments
Michael Lindon, Dae Woong Ham, Martin Tingley +1
Linear models are foundational tools in statistics and ubiquitous across the applied sciences. However, conventional statistical inference -- such as -tests and -tests -- are…
Benefits and costs of matching prior to a Difference in Differences analysis when parallel trends does not hold
Dae Woong Ham, Luke Miratrix
The Difference in Difference (DiD) estimator is a popular estimator built on the "parallel trends" assumption, which is an assertion that the treatment group, absent treatment, wou…
Hypothesis Testing in Sequentially Sampled Data: AdapRT to Maximize Power Beyond iid Sampling
Dae Woong Ham, Jiaze Qiu
Testing whether a variable of interest affects the outcome is one of the most fundamental problem in statistics and is often the main scientific question of interest. To tackle thi…
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
Dae Woong Ham, Kosuke Imai, Lucas Janson
Conjoint analysis is a popular experimental design used to measure multidimensional preferences. Researchers examine how varying a factor of interest, while controlling for other r…