Essays in Causal and Comparative Econometrics
type: CreativeWork
description: My doctoral dissertation concerns causal and comparative econometrics. It comprises four separate essays with a common theme. They propose new metrics for causal or comparative inference and address problems with some ratios that are widely used for policy analysis. In the first chapter titled 'Stable Probability Weighting for Large-Sample Causal Analysis,' I propose new methods for causal analysis involving "Basu's elephants" (data with extreme selection on observables), which plague many empirical studies in both social and health sciences. I address this empirically relevant issue of limited overlap in settings where unconfoundedness holds and the conditional average treatment effect can be parameterized. I do so by developing a general principle called "Stable Probability Weighting" (SPW) that can be used as an alternative to the widely used Inverse Probability Weighting (IPW) technique, which relies on strong overlap. I show that IPW (or its augmented version), when valid, is a special case of the more general SPW (or its doubly robust version), which adjusts for the extremeness of the conditional probabilities of the treatment states. My approach also provides doubly robust alternatives to the widely used "Robinson transformation," which lacks double robustness. Large-sample causal analysis based on the SPW principle can be implemented using several established parametric or semiparametric procedures for conditional moment models. My framework extends to the setting of multivalued treatments. In the second chapter titled 'Finite-Sample Set-Estimation and Inference for Causal Analysis,' I develop new finite-sample methods that apply when unconfoundedness is plausible within fine strata. Since the widely used IPW technique relies on the problematic reciprocal of the estimated propensity score, I develop alternative set-estimators that are unbiased in a sense. I also propose new finite-sample inference methods for testing a general class of weak null hypotheses. …
abstract: My doctoral dissertation concerns causal and comparative econometrics. It comprises four separate essays with a common theme. They propose new metrics for causal or comparative inference and address problems with some ratios that are widely used for policy analysis. In the first chapter titled 'Stable Probability Weighting for Large-Sample Causal Analysis,' I propose new methods for causal analysis involving "Basu's elephants" (data with extreme selection on observables), which plague many empirical studies in both social and health sciences. I address this empirically relevant issue of limited overlap in settings where unconfoundedness holds and the conditional average treatment effect can be parameterized. I do so by developing a general principle called "Stable Probability Weighting" (SPW) that can be used as an alternative to the widely used Inverse Probability Weighting (IPW) technique, which relies on strong overlap. I show that IPW (or its augmented version), when valid, is a special case of the more general SPW (or its doubly robust version), which adjusts for the extremeness of the conditional probabilities of the treatment states. My approach also provides doubly robust alternatives to the widely used "Robinson transformation," which lacks double robustness. Large-sample causal analysis based on the SPW principle can be implemented using several established parametric or semiparametric procedures for conditional moment models. My framework extends to the setting of multivalued treatments. In the second chapter titled 'Finite-Sample Set-Estimation and Inference for Causal Analysis,' I develop new finite-sample methods that apply when unconfoundedness is plausible within fine strata. Since the widely used IPW technique relies on the problematic reciprocal of the estimated propensity score, I develop alternative set-estimators that are unbiased in a sense. I also propose new finite-sample inference methods for testing a general class of weak null hypotheses. …
author: Karapakula, Venkatasai Ganesh
language: eng
keywords: Applied Econometrics, Axiomatic Framework, Causal Inference, Comparative Framework, Cost–Benefit Analysis, Econometrics, Economics, Equity–Efficiency Tradeoff, Finance, Finite-Sample Inference, Finite-Sample Set-Estimation, Friedman Test, Limited Overlap, Marginal Value of Public Funds, Multi-Output Model Selection, Multivariate Analysis, Multivariate Statistics, Portfolio Analysis, Public Economics, Ratings Analysis, Relative Policy Value, Relative Sign Value, Sector Analysis, Semiparametric Econometrics, Sign Test, Signed-Rank Test, Stable Probability Weighting, Statistical Inference, Statistics, Uniformly Valid Inference, Welfare Aggregation, Welfare Analysis
license: https://www.repository.cam.ac.uk/bitstreams/304ea009-eb4f-4bff-8450-5e095659c62f/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2025-11-02
date modified: 2026-07-22T22:24:24Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.123254