Discovering Causal Models with Optimization: Confounders, Cycles, and Feature Selection

50 Pages Posted: 8 Jul 2021 Last revised: 29 Jul 2021

See all articles by Frederick Eberhardt

Frederick Eberhardt

California Institute of Technology

Nur Kaynar

University of California, Los Angeles (UCLA) - Anderson School of Management

Auyon Siddiq

University of California, Los Angeles (UCLA) - Anderson School of Management

Date Written: June 24, 2021

Abstract

We propose a new method for learning causal structures from observational data, a process known as causal discovery. Our method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. We consider a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do. We formulate the discovery problem as an integer program, and propose a solution technique that leverages the conditional independence structure in the data to identify promising edges for inclusion in the output graph. In the large-sample limit, our method recovers a graph that is equivalent to the true data-generating graph. Computationally, our method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. We demonstrate our approach by showing how it can be used to examine the validity of instrumental variables, which are widely used for causal inference. In particular, we analyze US Census data from the seminal paper on the returns to education by Angrist and Krueger (1991), and find that the causal structures uncovered by our method are consistent with the literature.

Keywords: integer programming, causal inference, causal discovery, graphical models

Suggested Citation

Eberhardt, Frederick and Kaynar, Nur and Siddiq, Auyon, Discovering Causal Models with Optimization: Confounders, Cycles, and Feature Selection (June 24, 2021). Available at SSRN: https://ssrn.com/abstract=3873034 or http://dx.doi.org/10.2139/ssrn.3873034

Frederick Eberhardt

California Institute of Technology ( email )

Pasadena, CA 91125
United States

Nur Kaynar (Contact Author)

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Auyon Siddiq

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
397
Abstract Views
1,150
rank
110,243
PlumX Metrics