Causal inference is a powerful modeling tool for explanatory analysis, which might enable
current machine learning to become explainable. How to marry causal inference with …

Since the ground-breaking work of Saul Kripke, David Lewis, and others in the 1960s and
70s, one dominant interest of analytic philosophers has been in modal truths, which …

This is a comprehensive introduction to research design for university students at all levels
across the whole range of political science, including international relations and public …

Head hits cause brain damage-but not always. Should we ban sport to protect athletes?
Exposure to electromagnetic fields is strongly associated with cancer development-does that …

We argue for a theoretical link between the development of an extended period of immaturity
in human evolution and the emergence of powerful and wide-ranging causal learning …

Young children spend a large portion of their time pretending about non‐real situations.
Why? We answer this question by using the framework of Bayesian causal models to argue …

Counterfactual reasoning (CFR)—mentally representing what the world would be like now if
things had been different in the past—is an important aspect of human cognition and the …

How do people make causal judgments about physical events? We introduce the
counterfactual simulation model (CSM) which predicts causal judgments in physical settings …

Humans possess the remarkable capacity to imagine possible worlds and to demarcate
possibilities and impossibilities in reasoning. We can think about what might happen in the …

Pretend play universally emerges during early childhood and may support the development
of causal inference and counterfactual reasoning. However, the amount of time spent …