ZaliQL: Causal Inference from Observational Data at Scale

ZaliQL: A SQL-Based Framework for Drawing Causal Inference from Big Data

Causal inference from observational data is a subject of active research and development in statistics and computer science. Many toolkits have been developed for this purpose that depends on statistical software. However, these toolkits do not scale to large datasets. In this paper we describe a suite of techniques for expressing causal inference tasks from observational data in SQL. This suit...

Causal Structure Learning and Inference: A Selective Review

In this paper we give a review of recent causal inference methods. First, we discuss methods for causal structure learning from observational data when confounders are not present and have a close look at methods for exact identifiability. We then turn to methods which allow for a mix of observational and interventional data, where we also touch on active learning strategies. We also discuss me...

Title Methods for Graphical Models and Causal Inference

March 19, 2015 Version 2.0-10 Date 2015-03-18 Author Diego Colombo, Alain Hauser, Markus Kalisch, Martin Maechler Maintainer Markus Kalisch Title Methods for Graphical Models and Causal Inference Description Functions for causal structure learning and causal inference using graphical models. The main algorithms for causal structure learning are PC (for observational ...

The Limits of Causal Inference from Observational Data

Experimental Learning of Causal Models with Latent Variables

This article discusses graphical models that can handle latent variables without explicitly modeling them quantitatively. There exist several paradigms for such problem domains. Two of them are semi-Markovian causal models and maximal ancestral graphs. Applying these techniques to a problem domain consists of several steps, typically: structure learning from observational and experimental data,...