Many scientific questions are to understand and reveal the causal mechanisms from observational study data or experimental data. Over the past several decades, there has been a large number of developments to render causal inferences from observed data. Most developments are designed to estimate the mean difference between treated and control groups that is often called the average treatment effect (ATE), and rely on identifying assumptions to allow causal interpretation. However, more specific treatment effects beyond the ATE can be estimated under the same assumptions. For example, instead of estimating the mean of potential outcomes in a group, we may want to estimate the distribution of the potential outcomes. Understanding the distribution implies understanding the mean, but not vice versa. Therefore, more sophisticated causal inference can be made from the data. The dissertation focuses on causal inference in observational studies, and discusses three main achievements. First, in instrumental variable (IV) models, we propose a novel nonparametric likelihood method for estimating the distributional treatment effect that compares two potential outcome distributions for treated and control groups. Furthermore, we provide a nonparametric likelihood ratio test for the hypothesis that the two potential outcome distributions are identical. Second, we develop two methods for discovering effect modification in a matched observational study data: (1) the CART method and (2) the Submax method. Both methods are applied to real data examples for finding effect modifiers that alter the magnitude of the treatment effect. Lastly, we provide a causal definition of the malaria attributable fever fraction (MAFF) that has not been studied in the causal inference field, and propose a novel maximum likelihood method to account for fever killing effect and measurement errors.

The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.

This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments.

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.

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

In this dissertation, we develop improved estimation of average treatment effect on the treatment (ATT) which achieves double robustness, local efficiency, intrinsic efficiency and sample boundedness, using a calibrated likelihood approach. Moreover, we consider an extension of two-group causal inference problem to a general data combination problem, and develop estimators achieving desirable properties beyond double robustness and local efficiency. The proposed methods are shown, both theoretically and numerically, to be superior in robustness, efficiency or both to various existing estimators. In the first part, we review existing estimators on average treatment effect (ATE), mainly based on Tan (2006, 2010). This review provides a useful basis for improved estimation of average treatment effect on the treated (ATT). In the second part, we propose new methods to estimate the average treatment effect on the treated (ATT), which is of extensive interest in Econometrics, Biostatistics and other research fields. This problem seems to be often treated as a simple modification or extension of that of estimating overall average treatment effects (ATE). But the propensity score is no longer ancillary for estimation of ATT, in contrast with estimation of ATE. We study the efficient influence function and the corresponding semiparametric variance bound for the estimation of ATT under three different assumptions: a nonparametric model, a correct propensity score model and known propensity score. Then we construct Augmented Inverse Probability Weighted (AIPW) estimators which are locally efficient and doubly robust. Furthermore, we develop calibrated regression and likelihood estimators that are not only doubly robust and locally efficient, but also intrinsically e cient and sample bounded. Two simulations and real data analysis on a job training program are provided to demonstrate the advantage of our estimators compared with existing estimators. In the third part, we extend our methods to a general data combination problem for moment restriction models (Chen et al. 2008). Similarly, we derive augmented inverse probability weighted (AIPW) estimators that are locally efficient and doubly robust. Moreover, we develop calibrated regression and likelihood estimators which achieve double robustness, local efficiency and intrinsic efficiency. For illustration, we take the linear two-sample instrumental variable problem as an example, and derive all the relevant estimators by applying the general estimators in this specific example. Finally, a simulation study and an Econometric application on a public housing project are provided to demonstrate the superior performance of our improved estimators.

Explains the value of quasi-experimental techniques that can be used to approximate randomized experiments. The goal is to describe the logic of causal inference for researchers and policymakers who are not necessarily trained in experimental and quasi-experimental designs and statistical techniques.

In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.

Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.

Encompasses the main concepts and approaches of quantitative impact evaluations, used to consider the effectiveness of programmes, policies, projects or interventions. This textbook for economics graduate courses can also serve as a manual for professionals in research institutes, governments, and international organizations.