This paper analyzes generalization of the classic Rescorla-Wagner (RW) learning algorithm and studies their relationship to Maximum Likelihood estimation of causal parameters. We prove that the parameters of two popular causal models, ∆P and PC, can be learnt by the same generalized linear Rescorla-Wagner (GLRW) algorithm provided genericity conditions apply. We characterize the fixed points of...

(Anti)causal boundary conditions being imposed on the (seemingly) Hermitian Quantum Theory (HQT) as described in standard textbooks lead to an (Anti)Causal Quantum Theory ((A)CQT) with an indefinite metric (see e.g. [1–6]). Therefore, an (anti)causal neutral scalar field is not Hermitian, as one (anti)causal neutral scalar field consists of a non-Hermitian linear combination of two (Hermitian) ...

Discovering the causal mechanisms of biological systems is necessary to design new drugs and therapies. Computational Causal Discovery (CD) is a field that offers the potential to discover causal relations and causal models under certain conditions with a limited set of interventions / manipulations. This chapter reviews the basic concepts and principles of CD, the nature of the assumptions to ...

D. Hume (1739/1987) argued that causality is not observable. P. W. Cheng (1997) claimed to present "a theoretical solution to the problem of causal induction first posed by Hume more than two and a half centuries ago" (p. 398) in the form of the power PC theory (L. R. Novick & P. W. Cheng, 2004). This theory claims that people's goal in causal induction is to estimate causal powers from observa...

Temporal binding refers to the subjective contraction in time between an action and its consequence. Since it was reported in 2002 the effect has generated much interest, although a consensus regarding the mechanisms behind it remains elusive. While multiple theoretical accounts have been proposed, a key point of contention remains whether the effect is the result of the perception of intention...

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent g...

The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in comprehension caused by the outdated information (Exp...

We study the identifiability and estimation of functional causal models under selection bias, with a focus on the situation where the selection depends solely on the e↵ect variable, which is known as outcome-dependent selection. We address two questions of identifiability: the identifiability of the causal direction between two variables in the presence of selection bias, and, given the causal ...