Doctor of Philosophy / Department of Statistics / Michael J. Higgins / This dissertation presents an approach to assess and validate causal inference tools to es- timate the causal effect of a treatment. Finding treatment effects in observational studies is complicated by the need to control for confounders. Common approaches for controlling include using prognostically important covariates to form groups of similar units containing both treatment and control units or modeling responses through interpolation. This disser- tation proposes a series of new, computationally efficient methods to improve the analysis of observational studies. Treatment effects are only reliably estimated for a subpopulation under which a common support assumption holds—one in which treatment and control covariate spaces overlap. Given a distance metric measuring dissimilarity between units, a graph theory is used to find common support. An adjacency graph is constructed where edges are drawn between similar treated and control units to determine regions of common support by finding the largest connected components (LCC) of this graph. The results show that LCC improves on existing methods by efficiently constructing regions that preserve clustering in the data while ensuring interpretability of the region through the distance metric. This approach is extended to propose a new matching method called largest caliper matching (LCM). LCM is a version of cardinality matching—a type of matching used to maximize the number of units in an observational study under a covariate balance constraint between treatment groups. While traditional cardinality matching is an NP-hard, LCM can be completed in polynomial time. The performance of LCM with other five popular matching methods are shown through a series of Monte Carlo simulations. The performance of the simulations is measured by the bias, empirical standard deviation and the mean square error of the estimates under different treatment prevalence and different distributions of covariates. The formed matched samples improve estimation of the population treatment effect in a wide range of settings, and suggest cases in which certain matching algorithms perform better than others. Finally, this dissertation presents an application of LCC and matching methods on a study of the effectiveness of right heart catheterization (RHC) and find that clinical outcomes are significantly worse for patients that undergo RHC.

The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known factory example argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings.

The theories of causality put forward by Pearl and the Spirtes-Glymour-Scheines group have entered the mainstream of statistical thinking. These theories show that under ideal conditions, causal relationships can be inferred from purely statistical observational data. Nancy Cartwright advances certain arguments against these causal inference algorithms: the well-known factory example argument against the Causal Markov condition and an argument against faithfulness. We point to the dependence of the first argument on undefined categories external to the technical apparatus of causal inference algorithms. We acknowledge the possible practical implication of her second argument, yet we maintain, with respect to both arguments, that this variety of causal inference, if not universal, is nonetheless eminently useful. Cartwright argues against assumptions that are essential not only to causal inference algorithms but to causal inference generally, even if, as she contends, they are not without exception and that the same is true of other, likewise essential, assumptions. We indicate that causal inference is an iterative process and that causal inference algorithms assist, rather than replace, that process as performed by human beings.

No / Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics to infer causal relationships. The social and health sciences analyse population-level data using statistical methods to infer average causal relations. In diagnosis of disease, probabilistic statements are based on population-level causal knowledge combined with knowledge of a particular person¿s symptoms. For the physical sciences, the Salmon-Dowe account develops an analysis of causation based on the notion of process and interaction. In artificial intelligence, the development of graphical methods has leant impetus to a probabilistic analysis of causality. The biological sciences use probabilistic methods to look for evolutionary causes of the state of a current species and to look for genetic causal factors. This variegated situation raises at least two fundamental philosophical issues: about the relation between causality and probability, and about the interpretation of probability in causal analysis. In this book we bring philosophers and scientists together to discuss the relation between causality and probability, and the applications of these concepts within the sciences.

This dissertation addresses methodological problems in causal inference in the presence of time-varying confounding, and provides methodological tools to handle the problems within the potential outcomes framework of causal inference. The time-varying confounding is common in longitudinal observational studies, in which the covariates and treatments are interacting and changing over time in response to the intermediate outcomes and changing circumstances. The existing approaches in causal inference are mostly focused on static single-shot decision-making settings, and have limitations in estimating the effects of long-term treatments on the chronic problems. In this dissertation, I attempt to conceptualize the causal inference in this situation as a sequential decision problem, using the conceptual tools developed in decision theory, dynamic treatment regimes, and machine learning. I also provide methodological tools useful for this situation, especially when the treatments are multi-level and changing over time, using inverse probability weights and $g$-estimation. Substantively, this dissertation examines transitional justice's effects on human rights and democracy in emerging democracies. Using transitional justice as an example to illustrate the proposed methods, I conceptualize the adoption of transitional justice by a new government as a sequential decision-making process, and empirically examine the comparative effectiveness of transitional justice measures --- independently or in combination with others --- on human rights and democracy. / text

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This doctoral dissertation deals with causal modelling in the social sciences. The specific question addressed here is: what is the notion, or the rationale, of causality involved in causal models? The answer to that epistemological query emerges from a careful analysis of the social science methodology, of a number of paradigmatic case studies and of the philosophical literature. The main result is the development of the rationale of causality as the measure of variation. This rationale conveys the idea that to test – i.e. to confirm or disconfirm – causal hypotheses, social scientists test specific variations among variables of interest. The notion of variation is shown to be embedded in the scheme of reasoning of probabilistic theories of causality, in the logic of structural equation models and covariance structure models, and is also shown to be latent in many philosophical accounts. Further, the rationale of causality as measure of variation leaves room for a number of epistemological consequences about the warranty of the causal interpretation of structural models, the levels of causation, and the interpretation of probability. Firstly, it is argued that what guarantees the causal interpretation is the sophisticated apparatus of causal models, which is made of statistical, extra-statistical and causal assumptions, of a background context, of a conceptual hypothesis and of a hypothetico-deductive methodology. Next, a novel defence of twofold causality is provided and a principle to connect population-level causal claims and individual-level causal claims is offered. Last, a Bayesian interpretation of probability is defended, in particular, it is argued that empirically-based Bayesianism is the interpretation that best fit the epistemology of causality here presented. The rationale of variation is finally shown to be involved or at least consistent with a number of alternative accounts of causality; notably, with the mechanist and counterfactual approach, with agency-manipulability theories and epistemic causality, with singularist accounts and with causal analysis by contingency tables.

Causal uncertainty refers to the lack of confidence in one’s ability to understand causal relations in the social world (Weary & Edwards, 1994). Relative to people with low causal uncertainty, individuals with high causal uncertainty exhibit enhanced self-regulation performance following a social interaction (Jacobson, Papile, Passey, & Boucher, 2006). The current studies investigated the potential mechanisms underlying this relationship, and the role of self-esteem. Study 1 investigated whether the social or nonsocial nature of the depleting task and expectations about the need for future self-control could account for the relationship between causal uncertainty and self-regulation (N = 181). For the social task, high causally uncertain participants’ self-regulation performance was consistent across expectations for future self-control regardless of participant self-esteem. In contrast, low causally uncertain participants’ performance improved with increasing instructions to conserve energy for future tasks but only for participants with lower self-esteem. For low causally uncertain participants with higher self-esteem, self-regulation performance decreased with increased expectations for future self-control. In the nonsocial condition, the findings did not differ by self-esteem. Learning that the future task involved self-control and that the initial task was depleting were both associated with increases in self-regulation for high causally uncertain participants. In contrast, self-regulation abilities did not differ for low causally uncertain participants upon learning that the future task involved self-control and marginally decreased when they learned that the initial task was depleting. Study 2 examined whether or not self-presentation could account for the relationship between causal uncertainty and self-regulation abilities (N = 88). Higher causal uncertainty was associated with better self-regulation performance, but self-presentation goals did not moderate this relationship. Self-esteem did not influence self-regulation performance in this study. Study 3 investigated whether or not an accuracy goal could account for the relationship between causal uncertainty and self-regulation abilities (N = 112). For participants with lower self-esteem, high causally uncertain participants’ self-regulation performance was consistent regardless of the goal manipulation; whereas low causally uncertain participants’ performance improved with instructions to create accurate impressions of their partner. In contrast, for participants with higher self-esteem, self-regulation did not differ by causal uncertainty or goal conditions. / Thesis (Ph.D, Psychology) -- Queen's University, 2009-08-28 14:40:08.139