Causal inference is one of the most fundamental reasoning processes and one that is essential for question-answering as well as more general AI applications such as decision-making and diagnosis. Bayesian Networks are a popular formalism for encoding (probabilistic) causal knowledge that allows for inference. We developed a system for acquiring causal knowledge from text. Our system identifies ...

Research suggests that by the age of five, children have extensive causal knowledge, in the form of intuitive theories. The crucial question for developmental cognitive science is how young children are able to learn causal structure from evidence. Recently, researchers in computer science and statistics have developed representations (causal Bayes nets) and learning algorithms to infer causal ...

In order to estimate the causal effects of one or more exposures or treatments on an outcome of interest, one has to account for the effect of "confounding factors" which both covary with the exposures or treatments and are independent predictors of the outcome. In this paper we present regression methods which, in contrast to standard methods, adjust for the confounding effect of multiple cont...

We propose a new algorithm for estimating the causal structure that underlies the observed dependence among n (n ≥ 4) binary variables X1, . . . , Xn. Our inference principle states that the factorization of the joint probability into conditional probabilities for Xj given X1, . . . , Xj−1 often leads to simpler terms if the order of variables is compatible with the directed acyclic graph repre...

We study abductive, causal, and non-causal conditionals in indicative and counterfactual formulations using probabilistic truth table tasks under incomplete probabilistic knowledge (N = 80). We frame the task as a probability-logical inference problem. The most frequently observed response type across all conditions was a class of conditional event interpretations of conditionals; it was follow...