Causal programming: inference with structural causal models as finding instances of a relation

arXiv.org Artificial Intelligence

This paper proposes a causal inference relation and causal programming as general frameworks for causal inference with structural causal models. A tuple, $\langle M, I, Q, F \rangle$, is an instance of the relation if a formula, $F$, computes a causal query, $Q$, as a function of known population probabilities, $I$, in every model entailed by a set of model assumptions, $M$. Many problems in causal inference can be viewed as the problem of enumerating instances of the relation that satisfy given criteria. This unifies a number of previously studied problems, including causal effect identification, causal discovery and recovery from selection bias. In addition, the relation supports formalizing new problems in causal inference with structural causal models, such as the problem of research design. Causal programming is proposed as a further generalization of causal inference as the problem of finding optimal instances of the relation, with respect to a cost function.


Microsoft DoWhy is an Open Source Framework for Causal Reasoning

#artificialintelligence

The human mind has a remarkable ability to associate causes with a specific event. From the outcome of an election to an object dropping on the floor, we are constantly associating chains of events that cause a specific effect. Neuropsychology refers to this cognitive ability as causal reasoning. Computer science and economics study a specific form of causal reasoning known as causal inference which focuses on exploring relationships between two observed variables. Over the years, machine learning has produced many methods for causal inference but they remain mostly difficult to use in mainstream applications.


Causal Inference Using Machine Learning

#artificialintelligence

Finding causal relationships is the most profound motivation behind research in various disciplines. However, our toolbox of computational methods for causal inference has remained limited for a long time. Better computational methods for causal inference can change the way that we conduct research and have the potential to revolutionize science by expediting scientific discoveries and cutting costs.


Dynamic Causal Calculus

AAAI Conferences

We introduce dynamic causal calculus, a nonmonotonic formalism that can be viewed as a direct logical counterpart of the action description language C+. We formulate a nonmonotonic semantics of the associated causal language, and compare this semantics with the indirect, two-stage semantics for C+, given in (Giunchiglia et al 2004). It will be shown, in particular, that the suggested semantics allows us to alleviate syntactic distinctions between propositional atoms, maintained by C+, as well as type restrictions imposed on its causal laws. We will describe also a logical formalism of dynamic causal inference that constitutes a complete description of the logic that is adequate for this dynamic calculus.


A rational model of causal inference with continuous causes

Neural Information Processing Systems

Rational models of causal induction have been successful in accounting for people's judgments about the existence of causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2 2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of causal induction using continuous dimensions, which aims to diminish the gap between empirical and theoretical approaches and real-world causal induction. This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.