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Causal Inference in Equilibrium
Causal Inference in Equilibrium
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152115
- ISBN
- 9798384345039
- DDC
- 741
- 저자명
- Munro, Evan.
- 서명/저자
- Causal Inference in Equilibrium
- 발행사항
- [Sl] : Stanford University, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 172 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: A.
- 주기사항
- Advisor: Imbens, Guido;Wager, Stefan.
- 학위논문주기
- Thesis (Ph.D.)--Stanford University, 2024.
- 초록/해제
- 요약This dissertation contains three chapters, which each examine an aspect of casual inference when observed agents are interacting through an equilibrium. Chapter 1 studies the equilibrium of a Stackelberg game, Chapter 2 works with a general price equilibrium setting, and Chapter 3 considers equilibria of matching and auction markets.Chapter 1 has the title "Treatment Allocation with Strategic Agents". There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive Conditional Average Treatment Effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian Optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior.Chapter 2 has the title "Treatment Effects in Market Equilibrium" and is joint work with Stefan Wager and Kuang Xu. When randomized trials are run in a marketplace equilibriated by prices, interference arises. To analyze this, we build a stochastic model of treatment effects in equilibrium. We characterize the average direct (ADE) and indirect treatment effect (AIE) asymptotically. A standard RCT can consistently estimate the ADE, but confidence intervals and AIE estimation require price elasticity estimates, which we provide using a novel experimental design. We define heterogeneous treatment effects and derive an optimal targeting rule that meets an equilibrium stability condition. We illustrate our results using a freelance labor market simulation and data from a cash transfer experiment.Chapter 3 has the title "Causal Inference under Interference through Designed Markets". In many markets a centralized mechanism allocates goods. When an individual-level intervention affects submissions to the mechanism, program evaluation is challenging due to spillover effects that occur through the mechanism. We show that if the mechanism is truthful and has a "cutoff" structure, then it is possible to estimate the Global Treatment Effect (GTE) under a selection-on-observables assumption. Our proposed estimator is doubly-robust and semi-parametrically efficient. We also characterize heterogeneous treat ment effects and propose estimators for the optimal targeting rule in equilibrium. Adjusting for equilibrium effects notably diminishes the estimated effect of information on inequality in the Chilean school system.
- 일반주제명
- Design
- 일반주제명
- Probability
- 일반주제명
- Discount coupons
- 일반주제명
- Finance
- 기타저자
- Stanford University.
- 기본자료저록
- Dissertations Abstracts International. 86-03A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152115
■006m o d
■007cr#unu||||||||
■020 ▼a9798384345039
■035 ▼a(MiAaPQ)AAI31460275
■035 ▼a(MiAaPQ)Stanfordgw994jq1576
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a741
■1001 ▼aMunro, Evan.
■24510▼aCausal Inference in Equilibrium
■260 ▼a[Sl]▼bStanford University▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a172 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: A.
■500 ▼aAdvisor: Imbens, Guido;Wager, Stefan.
■5021 ▼aThesis (Ph.D.)--Stanford University, 2024.
■520 ▼aThis dissertation contains three chapters, which each examine an aspect of casual inference when observed agents are interacting through an equilibrium. Chapter 1 studies the equilibrium of a Stackelberg game, Chapter 2 works with a general price equilibrium setting, and Chapter 3 considers equilibria of matching and auction markets.Chapter 1 has the title "Treatment Allocation with Strategic Agents". There is increasing interest in allocating treatments based on observed individual characteristics: examples include targeted marketing, individualized credit offers, and heterogeneous pricing. Treatment personalization introduces incentives for individuals to modify their behavior to obtain a better treatment. Strategic behavior shifts the joint distribution of covariates and potential outcomes. The optimal rule without strategic behavior allocates treatments only to those with a positive Conditional Average Treatment Effect. With strategic behavior, we show that the optimal rule can involve randomization, allocating treatments with less than 100% probability even to those who respond positively on average to the treatment. We propose a sequential experiment based on Bayesian Optimization that converges to the optimal treatment rule without parametric assumptions on individual strategic behavior.Chapter 2 has the title "Treatment Effects in Market Equilibrium" and is joint work with Stefan Wager and Kuang Xu. When randomized trials are run in a marketplace equilibriated by prices, interference arises. To analyze this, we build a stochastic model of treatment effects in equilibrium. We characterize the average direct (ADE) and indirect treatment effect (AIE) asymptotically. A standard RCT can consistently estimate the ADE, but confidence intervals and AIE estimation require price elasticity estimates, which we provide using a novel experimental design. We define heterogeneous treatment effects and derive an optimal targeting rule that meets an equilibrium stability condition. We illustrate our results using a freelance labor market simulation and data from a cash transfer experiment.Chapter 3 has the title "Causal Inference under Interference through Designed Markets". In many markets a centralized mechanism allocates goods. When an individual-level intervention affects submissions to the mechanism, program evaluation is challenging due to spillover effects that occur through the mechanism. We show that if the mechanism is truthful and has a "cutoff" structure, then it is possible to estimate the Global Treatment Effect (GTE) under a selection-on-observables assumption. Our proposed estimator is doubly-robust and semi-parametrically efficient. We also characterize heterogeneous treat ment effects and propose estimators for the optimal targeting rule in equilibrium. Adjusting for equilibrium effects notably diminishes the estimated effect of information on inequality in the Chilean school system.
■590 ▼aSchool code: 0212.
■650 4▼aDesign
■650 4▼aProbability
■650 4▼aDiscount coupons
■650 4▼aFinance
■690 ▼a0389
■690 ▼a0508
■71020▼aStanford University.
■7730 ▼tDissertations Abstracts International▼g86-03A.
■790 ▼a0212
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162941▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
