Contingency and contiguity trade-offs in causal induction

Statistics in Medicine, 2005

Methodology for causal inference based on propensity scores has been developed and popularized in the last two decades. However, the majority of the methodology has concentrated on binary treatments. Only recently have these methods been extended to settings with multi-valued treatments. We propose a number of discrete choice models for estimating the propensity scores. The models di er in terms of exibility with respect to potential correlation between treatments, and, in turn, the accuracy of the estimated propensity scores. We present the e ects of discrete choice models used on performance of the causal estimators through a Monte Carlo study. We also illustrate the use of discrete choice models to estimate the e ect of antipsychotic drug use on the risk of diabetes in a cohort of adults with schizophrenia.

Observational Studies

Research questions that motivate most studies in statistics-based sciences are causal in nature. Economists and social scientists are typically interested in estimating causal effects rather than mere associations between variables (e.g., the effects of training programs on subsequent labor market histories); the same is true for epidemiologists and medical doctors (e.g., is smoking causing lung cancer? what is the effect of pollution on health outcomes?).

2011

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie), 2011

Rational models of causal induction have been successful in accounting for people's judgments about causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2× 2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of causal induction using continuous dimensions, which aims to diminish the gap between empirical and theoretical approaches and real-world causal induction. This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.

Educational Psychology Review, 2011

Causal prescriptive statements are valued in the social sciences when there is the goal of helping people through interventions. The articles in this special issue cover different methods for testing causal prescriptive statements. This commentary identifies both virtues and liabilities of these different approaches. We argue that it is extremely difficult to confirm causal prescriptive statements because of the potentially infinite number of third variables in causal relationships and of confounding variables in experimental manipulations. All methodological approaches, including randomized control trials, have a simple view of causality that does not adequately solve the challenges of indeterminacy, interactions, combinatorial explosion, and dynamics. However, these challenges should not discourage researchers from formulating and testing causal prescriptive statements.

Epidemiologic Perspectives & Innovations, 2009

As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice

2007

The literature on causal discovery has focused on interventions that involve randomly assigning values to a single variable. But such a randomized intervention is not the only possibility, nor is it always optimal. In some cases it is impossible or it would be unethical to perform such an intervention. We provide an account of'hard'and'soft'interventions and discuss what they can contribute to causal discovery.

The covariation component of everyday causal inference has been depicted, in both cognitive and social psychology as well as in philosophy, as heterogeneous and prone to biases. The models and biases discussed in these domains are analyzed with respect to focal sets: contextually determined sets of events over which covariation is computed. Moreover, these models are compared to our probabilistic contrast model, which specifies causes as first and higher order contrasts computed over events in a focal set. Contrary to the previous depiction of covariation computation, the present assessment indicates that a single normative mechanism-the computation of probabilistic contrasts-underlies this essential component of natural causal induction both in everyday and in scientific situations.

1997

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2005

Causal considerations must be relevant to making decisions. Nevertheless, traditional decision theories like evidential expected utility theory do not have the means to distinguish causal from merely evidential relations. As a result, they fail to distinguish cases where a choice influences consequences from cases where a choice does not affect consequences even though they are correlated. Therefore a causal model

Memory & Cognition, 2007

arXiv (Cornell University), 2023

Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups that isolate the average effect of treatment from confounding factors, randomization is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable features of randomized experiments, such as covariate balance, study representativeness, interpolated estimation, and unweighted analyses. We also discuss alternative regression modeling, weighting, and matching approaches and argue they should be given strong consideration in empirical work.

European Journal of Social Psychology, 2009

We investigate whether people prefer voluntary causes to physical causes in unfolding causal chains and whether statistical (covariation, sufficiency) principles can predict how people select explanations. Experiment 1 shows that while people tend to prefer a proximal (more recent) cause in chains of unfolding physical events, causality is traced through the proximal cause to an underlying distal (less recent) cause when that cause is a human action. Experiment 2 shows that causal preference is more strongly correlated with judgements of sufficiency and conditionalised sufficiency than with covariation or conditionalised covariation. In addition, sufficiency judgements are partial mediators of the effect of type of distal cause (voluntary or physical) on causal preference. The preference for voluntary causes to physical causes corroborates findings from social psychology, cognitive neuroscience and jurisprudence that emphasise the primacy of intentions in causal attribution processes.

When we try to identify causal relationships, how strong do we expect that relationship to be? Bayesian models of causal induction rely on assumptions regarding people’s a priori beliefs about causal systems, with recent research focusing on people’s expectations about the strength of causes. These expectations are expressed in terms of prior probability distributions. While proposals about the form of such prior distributions have been made previously, many different distributions are possible, making it difficult to test such proposals exhaustively. In Experiment 1 we used iterated learning—a method in which participants make inferences about data generated based on their own responses in previous trials— to estimate participants’ prior beliefs about the strengths of causes. This method produced estimated prior distributions that were quite different from those previously proposed in the literature. Experiment 2 collected a large set of human judgments on the strength of causal relationships to be used as a benchmark for evaluating different models, using stimuli that cover a wider and more systematic set of contingencies than previous research. Using these judgments, we evaluated the predictions of various Bayesian models. The Bayesian model with priors estimated via iterated learning compared favorably against the others. Experiment 3 estimated participants’ prior beliefs concerning different causal systems, revealing key similarities in their expectations across diverse scenarios.