Identification and Estimation of Causal Effects of Multiple Treatments Under the Conditional Independence Assumption The assumption that the assignment to treatments is ignorable conditional on attributes plays an important role in the applied statistic and econometric evaluation literature. Another term for it is conditional independence assumption. This paper discusses identification when the...

Note we are not asking whether aspirin cures headaches in some more general sense, we wish to know whether this specific headache went away because of the decision to take aspirin. The only sensible way to answer this sort of question in the case of a specific event is to compare the outcome which you observed with the counterfactual outcome which you would have observed if you had chosen not t...

Would the third-wave democracies have been democratized without prior modernization? What proportion of the past militarized disputes between nondemocracies would have been prevented had those dyads been democratic? Although political scientists often ask these questions of causal attribution, existing quantitative methods fail to address them. This article proposes an alternative statistical m...

In this report, I first review the evolution of ideas of causation as it relates to causal inference. Then I introduce two currently competing perspectives on this issue: the counterfactual perspective and the noncounterfactual perspective. The ideas of two statisticians, Donald B. Rubin, representing the counterfactual perspective, and A.P.Dawid, representing the noncounterfactual perspective ...

We report 4 studies which show that there are systematic quantitative patterns in the way we reason with uncertainty during causal and counterfactual inference. Two specific type of uncertainty – uncertainty about facts and about causal relations – are explored, and used to model people’s causal inferences (Studies 1-3). We then consider the relationship between causal and counterfactual reason...

In this paper we aim at finding conditions under which risk measures can be consistently updated. Since the class of risk measures that allow dynamically consistent updates appears to be limited, we consider weaker notions of consistency in addition to dynamic consistency, concentrating on notions that are still strong enough to ensure uniqueness of updating. We show that a weak type of consist...

Cartwright in her (2004) paper takes a pluralistic approach to causation. Inspired by Pearl’s (2000) multi-faceted notion of causation and Cartwright’s approach, I suggest a Causal Spectrum, a model in which all the causal models are arranged according to an agent’s amount and more importantly type of knowledge about a causal event. The more efficiently we use our knowledge of causal situations...

We present an integrated model for the understanding of and the reasoning from conditional statements. Central assumptions from several approaches are integrated into a causal path model. According to the model, the cognitive availability of exceptions to a conditional reduces the subjective conditional probability of the consequent, given the antecedent. This conditional probability determines...

Constraint-based causal structure learning algorithms rely on the faithfulness property. For faithfulness, all conditional independencies should come from the system’s causal structure. The problem is that even for linear Gaussian models the property is not tenable. In this paper, we identify 4 non-causal properties that generate conditional independencies and investigate whether they can be re...

Matched case-control study designs are commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case-control studies is a gain in efficiency. Methods for analyzing matched case-control studies have focused on utilizing conditional logistic regression models that provide conditional and not causal estimates...