{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129509"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129509","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Randomization-based inference for distributions and quantiles of individual treatment effects","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","abstract_has_math":false,"creators":["Su, Yongchang"],"institution":"University of Illinois Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Statistics","degree_department":null,"school":null,"contributors":["Li, Xinran","Yu, Ruoqi","Bowers, Jake","Shao, Xiaofeng"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-03-28","date_published":"2025-03-28","updated_at":"2026-07-22T22:25:05Z","subjects":["randomization inference","sensitivity analysis","multiple-choice knapsack problem","greedy algorithm","dynamic programming","stochastic dominance"],"languages":["en","eng"],"rights":["Copyright 2025 Yongchang Su"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/129509","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Li, Xinran","Yu, Ruoqi","Bowers, Jake","Shao, Xiaofeng"]},{"key":"dc:creator","label":"Author","values":["Su, Yongchang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-03-28","2025-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Statistics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["randomization inference","sensitivity analysis","multiple-choice knapsack problem","greedy algorithm","dynamic programming","stochastic dominance"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Yongchang Su"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/129509"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Yongchang Su, accepted the attached license on 2025-03-25 at 16:27.","The student, Yongchang Su, submitted this Dissertation for approval on 2025-03-25 at 16:56.","This Dissertation was approved for publication on 2025-03-28 at 09:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21692 on 2025-10-19 at 19:14:27","Chapter 1. Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy-tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual treatment effects, which can be more robust in the presence of extreme individual effects. Moreover, our inference for them is purely randomization-based, avoiding any distributional assumptions on the units. We first consider inference in stratified randomized experiments, extending the recent work by Caughey et al. (2023). We show that the computation of valid p-values for testing null hypotheses on quantiles of individual effects can be transformed into instances of the multiple-choice knapsack problem, which can be efficiently solved exactly or slightly conservatively. We then extend our approach to matched observational studies and propose sensitivity analysis to investigate to what extent our inference on quantiles of individual effects is robust to unmeasured confounding. The proposed randomization inference and sensitivity analysis are simultaneously valid for all quantiles of individual effects, noting that the analysis for the maximum or minimum individual effect coincides with the conventional analysis assuming constant treatment effects. Chapter 2. Stochastic dominance is a fundamental concept that has been widely studied in econometrics and policy evaluation. In this paper, we propose a method for testing the null hypothesis that the sample distribution of individual treatment effects is first-order stochastically dominated by a prespecified distribution. This hypothesis can also be interpreted as a simultaneous test on the quantiles of individual effects. To achieve this, we design a class of Mann-Whitney U-statistics that reformulates the computation of valid p-values as an assignment problem, which can be efficiently solved using the Hungarian algorithm. Throughout the paper, we propose multiple methods to improve the power of the p-values. We further extend our approach to test the stochastic dominance of the population distribution of individual treatment effects, assuming that the observed units are randomly sampled from population with finite or infinite size."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Randomization-based inference for distributions and quantiles of individual treatment effects"]}]}],"canonical_facts":{"dc:contributor":["Li, Xinran","Yu, Ruoqi","Bowers, Jake","Shao, Xiaofeng"],"dc:creator":["Su, Yongchang"],"dc:date":["2025-03-28","2025-05"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-05-01","The student, Yongchang Su, accepted the attached license on 2025-03-25 at 16:27.","The student, Yongchang Su, submitted this Dissertation for approval on 2025-03-25 at 16:56.","This Dissertation was approved for publication on 2025-03-28 at 09:35.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21692 on 2025-10-19 at 19:14:27","Chapter 1. Evaluating the treatment effect has become an important topic for many applications. However, most existing literature focuses mainly on average treatment effects. When the individual effects are heavy-tailed or have outlier values, not only may the average effect not be appropriate for summarizing treatment effects, but also the conventional inference for it can be sensitive and possibly invalid due to poor large-sample approximations. In this paper we focus on quantiles of individual treatment effects, which can be more robust in the presence of extreme individual effects. Moreover, our inference for them is purely randomization-based, avoiding any distributional assumptions on the units. We first consider inference in stratified randomized experiments, extending the recent work by Caughey et al. (2023). We show that the computation of valid p-values for testing null hypotheses on quantiles of individual effects can be transformed into instances of the multiple-choice knapsack problem, which can be efficiently solved exactly or slightly conservatively. We then extend our approach to matched observational studies and propose sensitivity analysis to investigate to what extent our inference on quantiles of individual effects is robust to unmeasured confounding. The proposed randomization inference and sensitivity analysis are simultaneously valid for all quantiles of individual effects, noting that the analysis for the maximum or minimum individual effect coincides with the conventional analysis assuming constant treatment effects. Chapter 2. Stochastic dominance is a fundamental concept that has been widely studied in econometrics and policy evaluation. In this paper, we propose a method for testing the null hypothesis that the sample distribution of individual treatment effects is first-order stochastically dominated by a prespecified distribution. This hypothesis can also be interpreted as a simultaneous test on the quantiles of individual effects. To achieve this, we design a class of Mann-Whitney U-statistics that reformulates the computation of valid p-values as an assignment problem, which can be efficiently solved using the Hungarian algorithm. Throughout the paper, we propose multiple methods to improve the power of the p-values. We further extend our approach to test the stochastic dominance of the population distribution of individual treatment effects, assuming that the observed units are randomly sampled from population with finite or infinite size."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129509"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Yongchang Su"],"dc:subject":["randomization inference","sensitivity analysis","multiple-choice knapsack problem","greedy algorithm","dynamic programming","stochastic dominance"],"dc:title":["Randomization-based inference for distributions and quantiles of individual treatment effects"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Statistics"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:05Z"}