A Linear “Microscope” for Interventions and Counterfactuals

A Linear “Microscope” for Interventions and Counterfactuals

Abstract: This note illustrates, using simple examples, how causal questions of non-trivial character can be represented, analyzed and solved using linear analysis and path diagrams. By producing closed form solutions, linear analysis allows for swift assessment of how various features of the model impact the questions under investigation. We discuss conditions for identifying total and direct ...

Embracing Events in Causal Modelling: Interventions and Counterfactuals in CP-Logic

This paper integrates Pearl’s seminal work on probability and causality with that of Shafer. Using the language of CP-logic, it transposes Pearl’s analysis of interventions and counterfactuals to the semantic context of Shafer’s probability trees. This gives us definitions that work not on the level of random variables, but on the level of Humean events. There are some tangible benefits to our ...

Causal Selection Vs Causal Parity in Biology: Relevant Counterfactuals and Biologically Normal Interventions

Probabilistic Networks for Automated Reasoning

2. A new type of causal models was formulated, based on embedding structural considerations in the language of sequential, temporal situation calculus. By using situation calculus as a basic language, we leverage its power to express complex, dynamically changing situations and, by relying on structural considerations, we can formulate an e ective theory of counterfactuals within the situationc...

Causal Inference

This paper reviews a theory of causal inference based on the Structural Causal Model (SCM) described in (Pearl, 2000a). The theory unifies the graphical, potential-outcome (NeymanRubin), decision analytical, and structural equation approaches to causation, and provides both a mathematical foundation and a friendly calculus for the analysis of causes and counterfactuals. In particular, the paper...