Causal inference, social networks and chain graphs

Social Mechanisms and Causal Inference

Several authors have claimed that mechanisms play a vital role in distinguishing between causation and mere correlation in the social sciences. Such claims are sometimes interpreted to mean that without mechanisms, causal inference in social science is impossible. The author agrees with critics of this proposition but explains how the account of how mechanisms aid causal inference can be interp...

Learning Causal AMP Chain Graphs

Andersson-Madigan-Perlman chain graphs were originally introduced to represent independence models. They have recently been shown to be suitable for representing causal models with additive noise. In this paper, we present an algorithm for learning causal chain graphs. The algorithm builds on the ideas by Hoyer et al. (2009), i.e. it exploits the nonlinearities in the data to identify the direc...

Signed directed acyclic graphs for causal inference.

Formal rules governing signed edges on causal directed acyclic graphs are described in this paper and it is shown how these rules can be useful in reasoning about causality. Specifically, the notions of a monotonic effect, a weak monotonic effect and a signed edge are introduced. Results are developed relating these monotonic effects and signed edges to the sign of the causal effect of an inter...

Causal inference in studies and medical articles

This article has no abstract.

Evaluating Causal effects using Chain Event Graphs