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Author: Harsh J. Parikh
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences based on incorrect assumptions frequently result in sub-optimal decisions with high penalties and long-term consequences. Unlike prediction or machine learning methods, it is particularly challenging to evaluate the performance of causal methods using just the observed data because the ground truth causal effects are missing for all units. My research presents frameworks to enable validation of causal inference methods in one of the following three ways: (i) auditing the estimation procedure by a domain expert, (ii) studying the performance using synthetic data, and (iii) using placebo tests to identify biases. This work enables decision-makers to reason about the validity of the estimation procedure by thinking carefully about the underlying assumptions. Our Learning-to-Match framework is an auditable-and-accurate approach that learns an optimal distance metric for estimating heterogeneous treatment effects. We augment Learning-to-Match framework with pharmacological mechanistic knowledge to study the long-term effects of untreated seizure-like brain activities in critically ill patients. Here, the auditability of the estimator allowed neurologists to qualitatively validate the analysis via a chart-review. We also propose Credence, a synthetic data based framework to validate causal inference methods. Credence simulates data that is stochastically indistinguishable from the observed data while allowing for user-designed treatment effects and selection biases. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications. We also discuss an approach to combines experimental and observational studies. Our approach provides a principled approach to test for the violations of key assumptions and estimate causal effects (Chapter 5).
Author: Harsh J. Parikh
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences based on incorrect assumptions frequently result in sub-optimal decisions with high penalties and long-term consequences. Unlike prediction or machine learning methods, it is particularly challenging to evaluate the performance of causal methods using just the observed data because the ground truth causal effects are missing for all units. My research presents frameworks to enable validation of causal inference methods in one of the following three ways: (i) auditing the estimation procedure by a domain expert, (ii) studying the performance using synthetic data, and (iii) using placebo tests to identify biases. This work enables decision-makers to reason about the validity of the estimation procedure by thinking carefully about the underlying assumptions. Our Learning-to-Match framework is an auditable-and-accurate approach that learns an optimal distance metric for estimating heterogeneous treatment effects. We augment Learning-to-Match framework with pharmacological mechanistic knowledge to study the long-term effects of untreated seizure-like brain activities in critically ill patients. Here, the auditability of the estimator allowed neurologists to qualitatively validate the analysis via a chart-review. We also propose Credence, a synthetic data based framework to validate causal inference methods. Credence simulates data that is stochastically indistinguishable from the observed data while allowing for user-designed treatment effects and selection biases. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications. We also discuss an approach to combines experimental and observational studies. Our approach provides a principled approach to test for the violations of key assumptions and estimate causal effects (Chapter 5).
Author: K. J. Rothman
Publisher: Kenneth Rothman
ISBN: 9780917227035
Category : Causation
Languages : en
Pages : 220
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Author: Leopoldo Bertossi
Publisher: Springer Nature
ISBN: 303131414X
Category : Computers
Languages : en
Pages : 219
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Book Description
The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers. The broad theme of this year's summer school was “Reasoning in Probabilistic Models and Machine Learning” and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures were presented during the school: Logic-Based Explainability in Machine Learning; Causal Explanations and Fairness in Data; Statistical Relational Extensions of Answer Set Programming; Vadalog: Its Extensions and Business Applications; Cross-Modal Knowledge Discovery, Inference, and Challenges; Reasoning with Tractable Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.
Author: José R. Zubizarreta
Publisher: CRC Press
ISBN: 1000850811
Category : Mathematics
Languages : en
Pages : 634
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Book Description
An observational study infers the effects caused by a treatment, policy, program, intervention, or exposure in a context in which randomized experimentation is unethical or impractical. One task in an observational study is to adjust for visible pretreatment differences between the treated and control groups. Multivariate matching and weighting are two modern forms of adjustment. This handbook provides a comprehensive survey of the most recent methods of adjustment by matching, weighting, machine learning and their combinations. Three additional chapters introduce the steps from association to causation that follow after adjustments are complete. When used alone, matching and weighting do not use outcome information, so they are part of the design of an observational study. When used in conjunction with models for the outcome, matching and weighting may enhance the robustness of model-based adjustments. The book is for researchers in medicine, economics, public health, psychology, epidemiology, public program evaluation, and statistics who examine evidence of the effects on human beings of treatments, policies or exposures.
Author: Audrey Amrein-Beardsley
Publisher: Routledge
ISBN: 1136702776
Category : Education
Languages : en
Pages : 275
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Book Description
Since passage of the of No Child Left Behind Act in 2001, academic researchers, econometricians, and statisticians have been exploring various analytical methods of documenting students‘ academic progress over time. Known as value-added models (VAMs), these methods are meant to measure the value a teacher or school adds to student learning from one year to the next. To date, however, there is very little evidence to support the trustworthiness of these models. What is becoming increasingly evident, yet often ignored mainly by policymakers, is that VAMs are 1) unreliable, 2) invalid, 3) nontransparent, 4) unfair, 5) fraught with measurement errors and 6) being inappropriately used to make consequential decisions regarding such things as teacher pay, retention, and termination. Unfortunately, their unintended consequences are not fully recognized at this point either. Given such, the timeliness of this well-researched and thoughtful book cannot be overstated. This book sheds important light on the debate surrounding VAMs and thereby offers states and practitioners a highly important resource from which they can move forward in more research-based ways.
Author: Aria Khademi
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Machine learning (ML) is transforming data-driven discovery and decision making across many areas of human endeavor. High stakes applications of machine learning, e.g., scientific discovery, healthcare, business decisions, etc., require the predictive models trained using ML to be interpretable by humans, and in many cases, free of undesirable biases that could lead to unfair discrimination on the basis of gender, race, and other protected attributes. This dissertation examines the closely related problems of model interpretability and fairness through a causal lens. The main contributions of the dissertation can be summarized as follows: (i) We reformulate the problem of fairness in decision making to that of estimating the causal effect of protected attributes on outcomes. We offer two causality-grounded measures for assessing fairness and show how to measure fairness by effectively and reliably estimating these definitions from observational data, in the absence of randomized controlled trials, using the Rubin-Neyman potential outcomes framework. (ii) We reformulate the problem of explaining the predictions of black box predictive models trained using machine learning, e.g., deep neural networks, to that of elucidating the causal effects of the inputs of the predictive model on its outputs. We offer the first model agnostic causality-based approach to interpreting black box models without access to the internal structure and parameters of the model. Our proposed solution can in principle be applied to any given black box model. We show how to reliably interpret predictions of deep neural networks using our proposed approach. (iii) We apply the tools of causal inference to gain insights into the role of different factors in the spread of COVID-19. Our analyses show that commuting ties play an important role in both the spread of COVID-19, and the deaths resulting from it.
Author: Jonas Peters
Publisher: MIT Press
ISBN: 0262037319
Category : Computers
Languages : en
Pages : 289
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Book Description
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning. The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data. After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem. The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
Author: Matheus Facure
Publisher: "O'Reilly Media, Inc."
ISBN: 1098140214
Category :
Languages : en
Pages : 428
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Book Description
How many buyers will an additional dollar of online marketing bring in? Which customers will only buy when given a discount coupon? How do you establish an optimal pricing strategy? The best way to determine how the levers at our disposal affect the business metrics we want to drive is through causal inference. In this book, author Matheus Facure, senior data scientist at Nubank, explains the largely untapped potential of causal inference for estimating impacts and effects. Managers, data scientists, and business analysts will learn classical causal inference methods like randomized control trials (A/B tests), linear regression, propensity score, synthetic controls, and difference-in-differences. Each method is accompanied by an application in the industry to serve as a grounding example. With this book, you will: Learn how to use basic concepts of causal inference Frame a business problem as a causal inference problem Understand how bias gets in the way of causal inference Learn how causal effects can differ from person to person Use repeated observations of the same customers across time to adjust for biases Understand how causal effects differ across geographic locations Examine noncompliance bias and effect dilution
Author: Andreas Holzinger
Publisher: Springer Nature
ISBN: 303104083X
Category : Artificial intelligence
Languages : en
Pages : 397
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Book Description
This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.
Author: Ioana Bica
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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