OPERATIONS, TECHNOLOGY, AND INFORMATION MANAGEMENT


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


NUR KAYNAR ASSISTANT PROFESSOR


Samuel Curtis Johnson Graduate School of Management


Cornell SC Johnson College of Business Cornell University


Management Science, 71, 4, April 2025 LINK TO PAPER LINK TO NUR KAYNAR VIDEO


Co-authors • Nur Kaynar


Assistant Professor, Samuel Curtis Johnson Graduate School of Management, Cornell SC Johnson College of Business, Cornell University


• Frederick Eberhardt, California Institute of Technology, Pasadena, CA • Auyon Siddiq, Anderson School of Management, University of California at Los Angeles


Summary Te authors propose a new optimization-based method for learning causal


structures from observational data, a process known as causal discovery. Teir method takes as input observational data over a set of variables and returns a graph in which causal relations are specified by directed edges. Considering a highly general search space that accommodates latent confounders and feedback cycles, which few extant methods do, they 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 edg- es for inclusion in the output graph.


In the large-sample limit, this method recovers a graph that is (Markov) equivalent to the true data-generating graph. Computationally, the method is competitive with the state-of-the-art, and can solve in minutes instances that are intractable for alternative causal discovery methods. Te authors leverage their method to develop a procedure for investigating the validity of an instru- mental variable and demonstrate it on the influential quarter-of-birth and proximity-to-college instruments for estimating the returns to education. In particular, this 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.


CONTENTS TO MAIN | RESEARCH WITH IMPACT: CORNELL SC JOHNSON COLLEGE OF BUSINESS • 2025 EDITION 47


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