Nur Kaynar is an assistant professor of Operations, Technology, and Information Management at the SC Johnson Graduate School of Management at Cornell University. Her research is at the intersection of optimization, causal inference, and operations. Specifically, she is interested in blending techniques from econometrics and machine learning to achieve efficient causal inference methods. At Johnson, she has taught an elective course on programming with Python in the full-time MBA program. Before joining Cornell, she received a Ph.D. from the Anderson School of Management at UCLA.
Talk: Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity
Abstract: We propose a new optimization-based method for learning causal structures from observational data. 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 graphical test for the validity of an instrumental variable and demonstrate it on the influential instruments for estimating the returns to education from Angrist and Krueger (1991) and Card (1993). In particular, our test complements existing instrument tests by revealing the precise causal pathways that undermine instrument validity, highlighting the unique merits of the graphical perspective on causality.