of the Dissertation Generalizability in Causal Inference: Theory and Algorithms

For many years, meeting satisfaction has been a key outcome variable in experimental Group Support Systems (GSS) research. GSS research results on meeting satisfaction are conflicting, reporting positive, negative, or no effects. Unfortunately, no causal model of meeting satisfaction has been developed that could explain these effects. This paper derives Satisfaction Attainment Theory (SAT), a ...

We investigate how people use causal knowledge to design interventions to affect the outcomes of causal systems. We propose that in addition to using content or mechanism knowledge to evaluate the effectiveness of interventions, people are also influenced by the abstract structural properties of a causal system. In particular, we investigated two factors that influence whether people tend to in...

We present a probabilistic theory of causal explanations, which integrates probabilistic and causal knowledge. Unlike most other approaches where a causal explanation is a hypothesis that one or more causative events occurred , we deene an explanation of a set of observations to be the occurrence of a chain of causation events. These causation events constitute a scenario where all the observat...

The micro-macro paradox has been revived. Despite broadly positive evaluations at the micro and meso-levels, recent literature has turned decidedly pessimistic with respect to the ability of foreign aid to foster economic growth. Policy implications, such as the complete cessation of aid to Africa, are being drawn on the basis of fragile evidence. This paper first assesses the aid-growth litera...

The Markov condition describes the conditional independence relations present in a causal model that are consequent to its graphical structure, whereas the faithfulness assumption presumes that there are no other independencies in the model. Cartwright argues that causal inference methods have limited applicability because the Markov condition cannot always be applied to domains, and gives an e...

An important goal in both transfer learning and causal inference is to make accurate predictions when the distribution of the test set and the training set(s) differ. Such a distribution shift may happen as a result of an external intervention on the data generating process, causing certain aspects of the distribution to change, and others to remain invariant. We consider a class of causal tran...

Typically, in the practice of causal inference from observational studies, a parametric model is assumed for the joint population density of potential outcomes and treatment assignments, and possibly this is accompanied by the assumption of no hidden bias. However, both assumptions are questionable for real data, the accuracy of causal inference is compromised when the data violates either assu...

This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (NeymanRubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper...

Actuarial risk assessments might be unduly perceived as a neutral way to counteract implicit bias and increase the fairness of decisions made at almost every juncture of the criminal justice system, from pretrial release to sentencing, parole and probation. In recent times these assessments have come under increased scrutiny, as critics claim that the statistical techniques underlying them migh...