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.
Results
Showing 1 to 10 of 10 for “"Unobserved Confounding"”.
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CAUSAL INFERENCE METHODS FOR ELECTRICAL CONSUMPTION’S ESTIMATION
… consumption and addressing issues like unobserved confounding and selection bias.
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Inference in tough places : essays on modeling and matching with applications to civil conflict
… inferences from observational data: even when unobserved confounding can be ruled out, correctly "conditioning on" or "adjusting for" covariates remains a challenge. In all but the simplest cases, existing methods ensure unbiased estimation only when the investigator can correctly specify the …
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Empirical Essays on Causal Effects of Job Training Programs: Evidence From Korea
… that confirms our results are robust to unobserved confounding. The second chapter analyzes the heterogeneous treatment effects of the programs on employability using a recent causal forest estimator, which is a machine learning technique. This chapter finds that almost a third of …
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From Theory to Practice: Improving Causal Conclusions from Healthcare Data
… selection and adjustment, proximal inference for unobserved confounding, and applications of modern estimation techniques to healthcare-relevant settings. In Chapter 2, we investigate the performance and robustness of state-of-the-art machine learning estimators for causal inference when covariate …
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Causal Structure Learning through Double Machine Learning
… on correlation instead of cause-effect one, ii) unobserved confounders may induce biases for the algorithms, leading to false causal inferences instead of revealing the correct causal structure, like a hidden common confounder, iii) the number of potential underlying structures increases …
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Algorithmic Approaches to Nonparametric Causal Inference
… the signal-to-noise ratio while accounting for unobserved confounding. We analyze the asymptotic distributional behavior of the algorithm's output to develop asymptotically valid hypothesis tests for causal effects. The resulting procedure achieves the maximal design sensitivity over a broad …
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Identification and Estimation with Deconfounded Instruments
… of a novel methodology, called common confounding (CC), for identifying and estimating the causal effects of endogenous (treatment) variables on an outcome variable with partially endogenous instrumental variables. A crucial estimation step called deconfounding recovers variation in the …
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Hypothesis testing and causal inference with heterogeneous medical data
… with a special focus on the influence of unobserved confounders that distort the observed associations between variables and yet may not be ruled out or adjusted for using data alone. We start by demonstrating that unobserved confounders may bias substantially the generalization …
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Essays in Econometrics: Nonparametrics and Robustness
… Proxy controls are informative proxies for unobserved confounding factors. For example, suppose we wish to estimate the causal impact of holding students back a grade on their future test scores. Academic ability is likely a confounding factor. While ability is not observed, early test …
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Representation Learning Based Causal Inference in Observational Studies
… controlled experimentation is infeasible and confounding is the main hurdle in estimating causal effect. As such, deconfounding constructs the main subject of this dissertation, that is (i) to restore the covariate balance between treatment groups and (ii) to attenuate spurious correlations in …