Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity

Frederick Eberhardt (), Nur Kaynar () and Auyon Siddiq ()
Additional contact information
Frederick Eberhardt: Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, California 91125
Nur Kaynar: Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853
Auyon Siddiq: Anderson School of Management, University of California, Los Angeles, Los Angeles, California 90095

Management Science, 2025, vol. 71, issue 4, 3283-3302

Abstract: We propose a new optimization-based 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 exploits 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 (Markov) 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 leverage our method to develop a procedure for investigating the validity of an instrumental variable and demonstrate it on the influential quarter-of-birth and proximity-to-college instruments for estimating the returns to education. In particular, our procedure complements existing instrument tests by revealing the precise causal pathways that undermine instrument validity, highlighting the unique merits of the graphical perspective on causality.

Keywords: programming: integer; networks-graphs: theory; economics: econometrics; statistics (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.02066 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:71:y:2025:i:4:p:3283-3302

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().