A successful theory of causal reasoning should be able to account for inferences about counterfactual scenarios. Pearl (2000) has developed a formal account of causal reasoning that has been highly influential but that suffers from at least two limitations as an account of counterfactual reasoning: it does not distinguish between counterfactual observations and counterfactual interventions, and...

We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph. We consider the application of generating faces based on given binary labe...

Biology is characterized by complex interactions between phenotypes, such as recursive and simultaneous relationships between substrates and enzymes in biochemical systems. Structural equation models (SEMs) can be used to study such relationships in multivariate analyses, e.g., with multiple traits in a quantitative genetics context. Nonetheless, the number of different recursive causal structu...

Given two variables that causally influence a binary response, we formalize the idea that their effects operate through a common mechanism, in which case we say that the two variables interact mechanistically. We introduce a mechanistic interaction relationship of "interference" that is asymmetric in the two causal factors. Conditions and assumptions under which such mechanistic interaction can...

This paper describes (,:LBURl DAN an implementf'd planner for problem domains in whicb tbe agent is uncertain about the initial world state and the effects of its own actions, but has sensors that allow it to improve its state of information. The system uses a probabilistir semantics to represent incomplete information, and provid('s for actions with informational as well as causal effects. The...

In everyday conversation "if" is one of the most frequently used conjunctions. This dissertation investigates what meaning an everyday conditional transmits and what inferences it licenses. It is suggested that the nature of the relation between the two propositions in a conditional might play a major role for both questions. Thus, in the experiments reported here conditional statements that de...

In an increasingly common class of studies, the goal is to evaluate causal effects of treatments that are only partially controlled by the investigator. In such studies there are two conflicting features: (1) a model on the full design and data can identify the causal effects of interest, but the model’s use in extreme regions of the data (e.g., where the outcome of interest is rare) can be sen...

Given causal graph assumptions, intervention-specific counterfactual distributions of the data can be defined by the so called G-computation formula, which is obtained by carrying out these interventions on the likelihood of the data factorized according to the causal graph. The obtained G-computation formula represents the counterfactual distribution the data would have had if this interventio...