In this section we describe the details of how to utilize the FCI algorithm to make inferences â€˜Â±â€™, â€˜0â€™, or â€˜?â€™, as mentioned in Section 5 of the article. Without going into all the details of theâ€¦ (More)

This paper reviews a class of methods to perform causal inference in the framework of a structural vector autoregressive model. We consider three different settings. In the first setting theâ€¦ (More)

The machine learning community has recently devoted much attention to the problem of inferring causal relationships from statistical data. Most of this work has focused on uncovering connectionsâ€¦ (More)

This document contains supplementary material to the article â€˜Statistical test for consistent estimation of causal effects in linear non-Gaussian modelsâ€™, AISTATS 2012. A table of contents is givenâ€¦ (More)

We adapt the Fast Causal Inference (FCI) algorithm of Spirtes et al. (2000) to the problem of inferring causal relationships from time series data and evaluate our adaptation and the original FCIâ€¦ (More)

In many fields of science, researchers are keen to learn causal connections among quantities of interest. For instance, in medical studies doctors want to infer the effect of a new drug on theâ€¦ (More)