Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 13 of 13 for “"double machine learning"”.
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Causal Structure Learning through Double Machine Learning
Learning the causal structure of a system solely from observational data is a fundamental yet intricate task with numerous applications across various fields, including economics, earth sciences, biology, and medicine. This task is challenging due to several reasons: i) observational data alone, as …
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Under-Coverage of Double Machine Learning Due to Implementation Choices
Double ML estimators can estimate coefficients of interest with far fewer functional form assumptions than linear econometric methods. However, DML requires researchers to make a range of implementation choices, including the selection of the function class, the random seed, and hyperparameter …
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Evaluating the Effects of Financial Deregulation on Bank Risk using Double Machine Learning
… developments in causal inference, particularly Double Machine Learning (DML), to more accurately estimate treatment effects. DML leverages machine learning algorithms to flexibly model both treatment and outcome processes, controlling for bias via orthogonaliza- tion techniques and …
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The Application of Double Machine Learning Onto Genomics Data Associated with Amyotrophic Lateral Sclerosis
… associated with ALS and previous work have used machine learning to try and determine the causal features of ALS. In this thesis we experiment with Double Machine Learning [8] to find causal features of ALS. We apply this method on both synthetic and real datasets that are associated with ALS and …
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Essays on Technological Change and Labor Market Dynamics: Causal Evidence from Remote Work and Automation
… framework augmented with double machine learning and an instrumental variables strategy, I find that shifting jobs from fully on-site to arrangements that include remote work increases the female share of new hires by about 10 percentage points and the female workforce …
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Essays on Measuring Climate Change Damages and Adaptation
… with Sylvia Klosin, I develop a novel debiased machine learning approach to measure continuous treatment effects in panel settings. We demonstrate benefits of this estimator over standard machine learning or classical statistics approaches. We apply this estimator to measure the degree of …
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High-Dimensional Statistics for Causal Inference and Panel Data
… panel data with fixed effects. We extend the double debiased machine learning (DML) framework to this setting and prove consistency and asymptotic normality. In an application to U.S. agriculture, we show that our estimator captures nonlinear effects of temperature on crop yields more …
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Leveraging Machine Learning and Causal Inference for Loan Default Prediction
… research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on …
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REPURPOSING DEEPFAKES FOR SOCIAL GOOD: THEORY, EMPIRICAL EVIDENCE, AND AI SYSTEMS FOR BIAS MEASUREMENT AND MITIGATION
… developing Deepfake-Informed Control Encoder for Double Machine Learning, and applies it to estimate the causal effect of skin tone on user engagement with 232,089 Instagram posts. Despite deepfakes' widespread association with malicious applications, this research demonstrates how controversial …
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Essays on Regulations in Peer-To-Peer Markets
… the model and automate this process by using Machine Learning (ML) techniques. I apply the Double Machine Learning (DML) approach proposed by Chernozhukov et al. (2018) and Chernozhukov et al. (2017), to a massive data of Airbnb rentals in the New York City (NYC) and estimate the demand facing …
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Predictive and Prescriptive Analytics in Operations Management
… decision making. This thesis proposes novel Machine Learning (ML) and optimization methods in (i) predictive analytics, (ii) prescriptive analytics, and (iii) their high-impact applications in operations management. On the predictive side, this thesis tackles the problems of interpretability …
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Essays in industrial organization
… and employs causal forest, a novel causal machine learning method, to estimate heterogeneous user click rates based on extensive browsing and ad exposure data from more than 1.6 million internet users. Simulations of user and platform behavior reveal that hypothetical mergers could boost …