Discovering Causal Models with Optimization: Confounders, Cycles, and Instrument Validity
提出一种基于优化的因果结构学习方法，能处理潜在混杂因素和反馈循环，并利用该方法开发了检验工具变量有效性的程序，应用于教育回报估计中的经典工具变量。
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. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.02066 .