Download or read book Essays on Using Machine Learning for Causal Inference written by Daniel Jacob and published by . This book was released on 2021* with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Three Essays on Causal Inference with High dimensional Data and Machine Learning Methods written by Neng-Chieh Chang and published by . This book was released on 2020 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three chapters that study causal inference when applying machinelearning methods. In Chapter 1, I propose an orthogonal extension of the semiparametric difference-in-differences estimator proposed in Abadie (2005). The proposed estimator enjoys the so-called Neyman-orthogonality (Chernozhukov et al. 2018) and thus it allows researchers to flexibly use a rich set of machine learning (ML) methods in the first-step estimation. It is particularly useful when researchers confront a high-dimensional data set when the number of potential control variables is larger than the sample size and the conventional nonparametric estimation methods, such as kernel and sieve estimators, do not apply. I apply this orthogonal difference-in-differences estimator to evaluate the effect of tariff reduction on corruption. The empirical results show that tariff reduction decreases corruption in large magnitude. In Chapter 2, I study the estimation and inference of the mode treatment effect. Mean,median, and mode are three essential measures of the centrality of probability distributions. In program evaluation, the average treatment effect (mean) and the quantile treatment effect (median) have been intensively studied in the past decades. The mode treatment effect, however, has long been neglected in program evaluation. This paper fills the gap by discussing both the estimation and inference of the mode treatment effect. I propose both traditional kernel and machine learning methods to estimate the mode treatment effect. I also derive the asymptotic properties of the proposed estimators and find that both estimators follow the asymptotic normality but with the rate of convergence slower than the regular rate N^1/2, which is different from the rates of the classical average and quantile treatment effect estimators. In Chapter 3 (joint with Liqiang Shi), we study the estimation and inference of the doublyrobust extension of the semiparametric quantile treatment effect estimation discussed in Firpo (2007). This proposed estimator allows researchers to use a rich set of machine learning methods in the first-step estimation, while still obtaining valid inferences. Researchers can include as many control variables as they consider necessary, without worrying about the over-fitting problem which frequently happens in the traditional estimation methods. This paper complements Belloni et al. (2017), which provided a very general framework to discuss the estimation and inference of many different treatment effects when researchers apply machine learning methods.
Download or read book Essays on Econometrics Causal Inference and Machine Learning written by Rahul Singh (Econometrician) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The traditional tools of econometrics may be inadequate for modern data sets, for example the 2020 US Census, which will be deliberately corrupted by the Census Bureau in the interest of privacy. Meanwhile, the modern tools of machine learning may be inadequate for the traditional goals of policy evaluation, which are to measure cause and effect and to assess statistical significance. In this dissertation, I develop tools for flexible causal inference, weaving machine learning into econometrics and solving unique problems that arise at their intersection. Specifically, I work in three domains at the intersection between econometrics and machine learning: (Chapter 1) causal inference with privacy protected data, (Chapter 2) rigorous statistical guarantees for machine learning, and (Chapter 3) simple algorithms for complex causal problems. JEL: C81,C45,C26.
Download or read book From Causal Inference to Machine Learning written by Michael Rainer Johann Kaiser and published by . This book was released on 2020 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning for Causal Inference written by Sheng Li and published by Springer. This book was released on 2023-11-26 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields. Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
Download or read book Elements of Causal Inference written by Jonas Peters and published by MIT Press. This book was released on 2017-11-29 with total page 289 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Essays on Causal Inference and Econometrics written by Haitian Xie and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is a collection of three essays on the econometric analysis of causal inference methods. Chapter 1 examines the identification and estimation of the structural function in fuzzy RD designs with a continuous treatment variable. We show that the nonlinear and nonseparable structural function can be nonparametrically identified at the RD cutoff under shape restrictions, including monotonicity and smoothness conditions. Based on the nonparametric identification equation, we propose a three-step semiparametric estimation procedure and establish the asymptotic normality of the estimator. The semiparametric estimator achieves the same convergence rate as in the case of a binary treatment variable. As an application of the method, we estimate the causal effect of sleep time on health status by using the discontinuity in natural light timing at time zone boundaries. Chapter 2 examines the local linear regression (LLR) estimate of the conditional distribution function F(y|x). We derive three uniform convergence results: the uniform bias expansion, the uniform convergence rate, and the uniform asymptotic linear representation. The uniformity in the above results is with respect to both x and y and therefore has not previously been addressed in the literature on local polynomial regression. Such uniform convergence results are especially useful when the conditional distribution estimator is the first stage of a semiparametric estimator. Chapter 3 studies the estimation of causal parameters in the generalized local average treatment effect model, a generalization of the classical LATE model encompassing multi-valued treatment and instrument. We derive the efficient influence function (EIF) and the semiparametric efficiency bound for two types of parameters: local average structural function (LASF) and local average structural function for the treated (LASF-T). The moment condition generated by the EIF satisfies two robustness properties: double robustness and Neyman orthogonality. Based on the robust moment condition, we propose the double/debiased machine learning (DML) estimators for LASF and LASF-T. We also propose null-restricted inference methods that are robust against weak identification issues. As an empirical application, we study the effects across different sources of health insurance by applying the developed methods to the Oregon Health Insurance Experiment.
Download or read book Essays in Causal Inference written by Michael Pollmann and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation explores the estimation of causal effects in settings with non-standard data. In the first chapter, the treatments are not directly assigned to outcome units but instead occur in the same geographic space. In the second chapter, responses to hypothetical questions describing both the treated and control state are used to learn about the effects of a treatment on real behavior (outcomes). In the third chapter, treatments are assigned according to a randomized experiment but outcomes are heavy-tailed, such that semiparametric approaches are useful to improve efficiency and robustness. The first chapter considers settings where the treatments causing the effects of interest are not directly associated with specific units for which we measure outcomes, but rather occur in the same geographic space. Many events and policies (treatments), such as opening of businesses, building of hospitals, and sources of pollution, occur at specific spatial locations, with researchers interested in their effects on nearby individuals or businesses (outcome units). However, the existing treatment effects literature primarily considers treatments that could experimentally be assigned directly at the level of the outcome units, potentially with spillover effects. I approach the spatial treatment setting from a similar experimental perspective: What ideal experiment would we design to estimate the causal effects of spatial treatments? This perspective motivates a comparison between individuals near realized treatment locations and individuals near counterfactual (unrealized) candidate locations, which is distinct from current empirical practice. I derive standard errors based on this design-based perspective that are straightforward to compute irrespective of spatial correlations in outcomes. Furthermore, I propose machine learning methods to find counterfactual candidate locations and show how to apply the proposed methods on observational data. I study the causal effects of grocery stores on foot traffic to nearby businesses during COVID-19 shelter-in-place policies. I find a substantial positive effect at a very short distance. Correctly accounting for possible effect "interference" between grocery stores located close to one another is of first order importance when calculating standard errors in this application. The second chapter is co-authored with B. Douglas Bernheim, Daniel Björkegren, and Jeffrey Naecker. We explore methods for inferring the causal effects of treatments on choices by combining data on real choices with hypothetical evaluations. We propose a class of estimators, identify conditions under which they yield consistent estimates, and derive their asymptotic distributions. The approach is applicable in settings where standard methods cannot be used (e.g., due to the absence of helpful instruments, or because the treatment has not been implemented). It can recover heterogeneous treatment effects more comprehensively, and can improve precision. We provide proof of concept using data generated in a laboratory experiment and through a field application. The final chapter is co-authored with Susan Athey, Peter J. Bickel, Aiyou Chen, and Guido W. Imbens. We develop new semiparametric methods for estimating treatment effects. We focus on a setting where the outcome distributions may be heavy-tailed, where treatment effects are small, where sample sizes are large and where assignment is completely random. This setting is of particular interest in recent experimentation in tech companies. We propose using parametric models for the treatment effects, as opposed to parametric models for the full outcome distributions. This leads to semiparametric models for the outcome distributions. We derive the semiparametric efficiency bound for this setting, and propose efficient estimators. In the case with a constant treatment effect one of the proposed estimators has an interesting interpretation as a weighted average of quantile treatment effects, with the weights proportional to (minus) the second derivative of the log of the density of the potential outcomes. Our analysis also results in an extension of Huber's model and trimmed mean to include asymmetry and a simplified condition on linear combinations of order statistics, which may be of independent interest.
Download or read book Essays in Political Methodology written by Apoorva Lal and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation is comprised of four chapters, which are united by their focus on the applications of machine learning methods to improve the robustness and flexibility of causal inference strategies commonly used by social scientists. Chapter 1 proposes methods that use spatial smoothing techniques to flexibly adjust for unobserved spatial confounders for observational causal inference problems. Chapter 2 examines the robustness of instrumental variables strategies in a decade's worth of published articles in political science and provides guidelines for empirical practice related to inference with weak instruments. Chapter 3 introduces a framework for estimating treatment effects by combining predictions from machine learning models with weights that balance observable characteristics across treatment and control groups in a variety of data settings frequently encountered by social scientists. Chapter 4 applies these methods to the question of whether private election funding affected turnout and votes for the democratic party in the 2020 election, and finds scant evidence in favor of popular narratives that these grants swung the election.
Download or read book An Introduction to Causal Inference written by Judea Pearl and published by Createspace Independent Publishing Platform. This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called "causal effects" or "policy evaluation") (2) queries about probabilities of counterfactuals, (including assessment of "regret," "attribution" or "causes of effects") and (3) queries about direct and indirect effects (also known as "mediation"). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1.
Download or read book Essays in Predictive and Causal Machine Learning written by Gabriel Okasa and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Cause Effect Pairs in Machine Learning written by Isabelle Guyon and published by Springer Nature. This book was released on 2019-10-22 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other. This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website. Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.
Download or read book Causal Inference in Python written by Matheus Facure and published by "O'Reilly Media, Inc.". This book was released on 2023-07-14 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Three Essays on Causal Inference for Marketing Applications written by Ashutosh Charudatta Bhave and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In my dissertation consisting of three research projects, I focus on solving problems which deal with reliably estimating the impact of a change in policy in quasi-experimental setup. I utilize cutting edge methods in econometrics and machine learning to quantify causal effects of policy changes, understand the mechanism behind the effect and most importantly highlight the implications for the managers and policy makers. My first research paper, “A Study of the Effects of Legalization of Recreational Marijuana on Sales of Cigarettes” attempts to establish a causal link between the legalization of recreational marijuana and the sales of cigarettes in retail stores. Recreational marijuana legalization (RML) has been on the rise in the recent years and many arguments have been put forth to support or counter this move. We explore the possibility of RML impacting cigarette consumption. This is important for understanding the impact on health care expenditures related to smoking, which is about $330 billion in the US. Our results show that in states that have passed RML, there is a 7% increase in cigarette sales. This is an important finding since it reverses a decline in cigarette sales in recent years. Therefore, we conclude that states should exercise caution while considering legalization of recreational use of marijuana. My second project, “Effects of Social Media Fights and New Product Launches in the Fast Food Industry” examines the effects of engaging in ‘Twitter feuds’ with competition during new product launches. We propose a viable mechanism that explains how seemingly harmless banter of social media could have unforeseen impact on a firm’s business. Through empirical evidence from recent incidents, we show that Twitter activity has a spillover into traditional media which leads to surge in online search. Online search activity is followed by the offline sales as documented in literature as well as evidenced from our unique foot traffic data. Next, we document the long-term effects of this menu innovation in causal framework, well beyond the initial frenzy, with a novel synthetic difference-in-differences (SDID) method proposed by Arkhangelsky et al. (2021). Results show that the launch led to a 30% increase in store visits up to six months after the launch. Overall, these findings underscore the importance of savvy social media presence especially during a product launch- which could be driver for peaked interest leading to impact on overall business. The flip side for competitors is that initiating seemingly harmless banter, unlike in the offline setting, could end up providing free publicity to one’s rivals. Overall, we highlight the enormous potential of social media to affect business and advise caution to brand managers before engaging in any activity. My third project “A study of wear out and heterogeneous effects of unlimited shipping program on customer engagement in the online retail industry” we study effects of a variation of free shipping promotion in the online retail industry. Free shipping promotions have become popular among online retailers. Most online shoppers expect deliveries without additional costs and cite it as a primary concern while shopping online. Many online retailers across industries have implemented long term free shipping programs on all purchases with fixed annual fees. In this paper, we analyze benefits associated with such programs for the retailers and also shed light on the potential pitfalls, using data from a leading online retailer in the UK. Our results indicate that that there is a significant decay in customer spending after initial days and the effects wear out completely short way through the promotion period. Moreover, changes in purchase behavior (significantly lower basket size after enrolling for free shipping) could hurt the retailer. Thus, online retailers should be cautious when offering long term free shipping promotion. In the next part of the paper, we use pre-promotion engagement as a moderating factor to capture heterogeneous effects of free shipping programs across customers, using Honest Causal Forests approach. Our results show that free shipping promotions work better (higher revenues, smaller drop in basket size) for customers with relatively lower engagement with the retailer in the prepromotion period. Online retailers could use these findings to devise their targeting strategy for free shipping promotions.
Download or read book Essays in Honor of Cheng Hsiao written by Dek Terrell and published by Emerald Group Publishing. This book was released on 2020-04-15 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: Including contributions spanning a variety of theoretical and applied topics in econometrics, this volume of Advances in Econometrics is published in honour of Cheng Hsiao.
Download or read book Essays in Causal Inference written by Claudia Luise Charlotte Noack and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Essays in Causal Inference written by Yoshiyasu Rai and published by . This book was released on 2019 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: In Chapter 1, I study the statistical inference problem for treatment assignment policies. In typical applications, individuals with different characteristics are expected to differ in their responses to treatment. As a result, treatment assignment policies that allocate treatment based on individuals' observed characteristics can have a significant influence on outcomes and welfare. A growing literature proposes various approaches to estimating the welfare-optimzing treatment assignment policy. I develop a method for assessing the precision of estimated optimal policies. In particular, for the welfare used by \cite{KT:18} to propose estimated assignment policy, my method constructs (i) a confidence set of policies that contains the optimal policy, which maximizes the average social welfare among all the feasible policies with prespecified level and (ii) a confidence interval for the maximized welfare. A simulation study indicates that the proposed methods work reasonably well with modest sample size. I apply the method to experimental data from the National Job Training Partnership Act study. In Chapter 2, I derive the large sample properties of $M$th nearest neighbor propensity score matching estimator with a potentially misspecified propensity score model. By using the local misspecification framework, I formalize the bias/variance trade-off with respect to the choice of propensity score estimator and propose a model selection criterion that aims to minimize the estimation error. Finally, in Chapter 3 (co-authored with Taisuke Otsu), we propose asymptotically valid inference methods for matching estimators based on the weighted bootstrap. The key is to construct bootstrap counterparts by resampling based on certain linear forms of the estimators. Our weighted bootstrap is applicable for the matching estimators of both the average treatment effect and its counterpart for the treated population. Also, by incorporating a bias correction method in \cite{AI:11}, our method can be asymptotically valid even for matching based on a vector of covariates. A simulation study indicates that the weighted bootstrap method is favorably comparable with the asymptotic normal approximation by \cite{AI:06}. As an empirical illustration, we apply the proposed method to the National Supported Work data.
