Comment It is a pleasure to comment on the paper by Antonio Forcina. Our comment is restricted to the example, discussed in section 3.3 of Forcina's paper, of a two-armed double-blind placebo-controlled randomized experiment in which: (i) no subject in the placebo arm takes active treatment, (ii) some subjects in the treatment arm do not comply and fail to take the active treatment, and (iii) t...

The application of the formal framework of causal Bayesian Networks to children's causal learning provides the motivation to examine the link between judgments about the causal structure of a system, and the ability to make inferences about interventions on components of the system. Three experiments examined whether children are able to make correct inferences about interventions on different ...

As the counterfactual model of causality has increased in popularity, sociologists have returned to matching as a research methodology. In this article, advances over the past two decades in matching estimators are explained, and the practical limitations of matching techniques are emphasized. The authors introduce matching methods by focusing first on ideal scenarios in which stratification an...

We extend Robins’ theory of causal inference for complex longitudinal data to the case of continuously varying as opposed to discrete covariates and treatments. In particular we establish versions of the key results of the discrete theory: the g-computation formula and a collection of powerful characterizations of the g-null hypothesis of no treatment effect. This is accomplished under natural ...

We describe an attempt to understand causal reasoning in situations where a binary cause produces a change on a continuous magnitude dimension. We consider established theories of binary probabilistic causal inference – ΔP and Power PC – and adapt them to continuous non-probabilistic outcomes. While ΔP describes causal strength as the difference of effect occurrence between the presence and abs...

Judea Pearl Cognitive Systems Laboratory Departments of Computer Science and Statistics University of California, Los Angeles, CA 90024 [email protected] In Journal of American Statistical Association (with discussions by TECHNICAL REPORT G. Casella, D. Cox, S. Greenland, J. Pearl, J. Robins, D. Rubin, R-269 S. Schwartz, G. Shafer, and L. Wasserman), Vol. 95, No. 450, 428{435, April 2000 June 2...

Cloud computing involves complex technical and economical systems and interactions. This brings about various challenges, two of which are: (1) debugging and control of computing systems, based on heterogeneous data, and (2) prediction of performance and price of “spot” resources, allocated via auctions. In this paper, we first establish two theoretical results on approximate causal inference. ...

This paper presents a computational model, via Logic Programming (LP), of counterfactual reasoning with applications to agent morality. Counterfactuals are conjectures about what would have happened, had an alternative event occurred. In the first part, we show how counterfactual reasoning, inspired by Pearl’s structural causal model of counterfactuals, is modeled using LP, by benefiting from L...

Researchers are often interested in treatment effects on outcomes that only defined conditional a post-treatment event status. For example, study of the effect different cancer treatments quality life at end follow-up, individuals who die during is undefined. In these settings, naive contrast variable not an average causal effect, even randomized experiment. Therefore principal stratum those wo...

We introduce causal implicit generative models (CiGMs): models that allow sampling from not only the true observational but also the true interventional distributions. We show that adversarial training can be used to learn a CiGM, if the generator architecture is structured based on a given causal graph. We consider the application of conditional and interventional sampling of face images with ...