Kevin Han

Kevin Han

Ph.D in statistics doing research in causal inference.

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Pages

Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Estimating Treatment Effects Using Observational Data and Experimental Data with Non-overlapping Support

arXiv:2202.13544, 2022

Given the richness of observational data and usefulness of experimental data, researchers hope to develop credible method to combine the strength of the two. In this paper, we consider a setting where the observational data contain the outcome of interest as well as a surrogate outcome while the experimental data contain only the surrogate outcome. We propose a simple estimator to estimate the average treatment effect of interest using both the observational data and the experimental data.

Estimation and Testing Methods for Causal Inference with Interference

Dissertation for PhD in Statistics, 2023

This dissertation offers new methodologies and theoretical results to address key issues in causal inference with interference. Specifically, we develop inferential results for causal effect estimators in panel experiments under interference, introduce novel estimation methods for causal effects with network experiments and tackle the problem of detecting interference in online controlled experiments with increasing allocation.

Ensemble Method for Estimating Individualized Treatment Effects

KDD Workshop on Causal Inference and Machine Learning in Practice, 2023

In contrast to several recent papers proposing methods to select one of these competing models, we propose an algorithm for aggregating the estimates from a diverse library of models. We compare ensembling to model selection on 43 benchmark datasets, and find that ensembling wins almost every time. Theoretically, we prove that our ensemble model is (asymptotically) at least as accurate as the best model under consideration, even if the number of candidate models is allowed to grow with the sample size.

Detecting Interference in Online Controlled Experiments with Increasing Allocation

ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023

In this work, we introduce a widely applicable procedure to test for interference in A/B testing with increasing allocation. Our procedure can be implemented on top of an existing A/B testing platform with a separate flow and does not require a priori a specific interference mechanism.

Model-Based Regression Adjustment with Model-Free Covariates for Network Interference

Journal of Causal Inference, 2023

In this work, we introduce a sequential procedure to generate and select graph- and treatment-based covariates for GATE estimation under regression adjustment. To tackle inferential complications caused by our feature generation and selection process, we introduce a way to construct confidence intervals based on a block bootstrap.

Population Interference in Panel Experiments

Journal of Econometrics, 2024

The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit’s outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in this setting are now readily recognized, and partial remedies are well known. Less understood is the impact of population interference in panel experiments where treatment is sequentially randomized in the population, and the outcomes are observed at each time step. This paper proposes a general framework for studying population interference in panel experiments and presents new finite population estimation and inference results. Our findings suggest that, under mild assumptions, the addition of a temporal dimension to an experiment alleviates some of the challenges of population interference for certain estimands. In contrast, we show that the presence of carryover effects — that is, when past treatments may affect future outcomes — exacerbates the problem. Our results are illustrated through both an empirical analysis and an extensive simulation study.

talks

teaching

MS&E 327: Topics in Causal Inference

Master/PhD course, Stanford University, Department of Management Science and Engineering </p>

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STATS 116: Theory of Probability

Undergraduate course, Stanford University, Department of Statistics </p>

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STATS 200: Introduction to Statistical Inference

Undergraduate/master course, Stanford University, Department of Statistics </p>

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STATS 202: Data Mining and Analysis

Undergraduate course, Stanford University, Department of Statistics </p>

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STATS 215: Statistical Models in Biology

Master/PhD course, Stanford University, Department of Statistics </p>

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Statistics 305A: Applied Statistics (Linear Models and More)

PhD course, Stanford University, Department of Statistics </p>

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