The Temporal Logic of Causal Structures

by   Samantha Kleinberg, et al.

Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene expression levels. However, it can be challenging to determine from just the numerical time course data alone what is coordinating the visible processes, to separate the underlying prima facie causes into genuine and spurious causes and to do so with a feasible computational complexity. For this purpose, we have been developing a novel algorithm based on a framework that combines notions of causality in philosophy with algorithmic approaches built on model checking and statistical techniques for multiple hypotheses testing. The causal relationships are described in terms of temporal logic formulae, reframing the inference problem in terms of model checking. The logic used, PCTL, allows description of both the time between cause and effect and the probability of this relationship being observed. We show that equipped with these causal formulae with their associated probabilities we may compute the average impact a cause makes to its effect and then discover statistically significant causes through the concepts of multiple hypothesis testing (treating each causal relationship as a hypothesis), and false discovery control. By exploring a well-chosen family of potentially all significant hypotheses with reasonably minimal description length, it is possible to tame the algorithm's computational complexity while exploring the nearly complete search-space of all prima facie causes. We have tested these ideas in a number of domains and illustrate them here with two examples.



There are no comments yet.


page 1

page 2

page 3

page 4


On probability-raising causality in Markov decision processes

The purpose of this paper is to introduce a notion of causality in Marko...

Causality-based Model Checking

Model checking is usually based on a comprehensive traversal of the stat...

Accounting for hidden common causes when infering cause and effect from observational data

Identifying causal relationships from observation data is difficult, in ...

Accounting for hidden common causes when inferring cause and effect from observational data

Identifying causal relationships from observation data is difficult, in ...

Distributions associated with simultaneous multiple hypothesis testing

We develop the distribution of the number of hypotheses found to be stat...

Causal Unfoldings and Disjunctive Causes

In the simplest form of event structure, a prime event structure, an eve...

Causal Explanations of Image Misclassifications

The causal explanation of image misclassifications is an understudied ni...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.