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1. A Computational Framework for Solving Nonlinear Binary Optimization Problems in Robust Causal Inference

   https://doi.org/10.1287/ijoc.2022.1226  

   Islam, Md Saiful ; Morshed, Md Sarowar ; Noor-E-Alam, Md. ( November 2022 , INFORMS Journal on Computing)

   Identifying cause-effect relations among variables is a key step in the decision-making process. Whereas causal inference requires randomized experiments, researchers and policy makers are increasingly using observational studies to test causal hypotheses due to the wide availability of data and the infeasibility of experiments. The matching method is the most used technique to make causal inference from observational data. However, the pair assignment process in one-to-one matching creates uncertainty in the inference because of different choices made by the experimenter. Recently, discrete optimization models have been proposed to tackle such uncertainty; however, they produce 0-1 nonlinear problems and lack scalability. In this work, we investigate this emerging data science problem and develop a unique computational framework to solve the robust causal inference test instances from observational data with continuous outcomes. In the proposed framework, we first reformulate the nonlinear binary optimization problems as feasibility problems. By leveraging the structure of the feasibility formulation, we develop greedy schemes that are efficient in solving robust test problems. In many cases, the proposed algorithms achieve a globally optimal solution. We perform experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms and compare our results with the state-of-the-art solver. Our experiments show that the proposed algorithms significantly outperform the exact method in terms of computation time while achieving the same conclusion for causal tests. Both numerical experiments and complexity analysis demonstrate that the proposed algorithms ensure the scalability required for harnessing the power of big data in the decision-making process. Finally, the proposed framework not only facilitates robust decision making through big-data causal inference, but it can also be utilized in developing efficient algorithms for other nonlinear optimization problems such as quadratic assignment problems. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the Division of Civil, Mechanical and Manufacturing Innovation of the National Science Foundation [Grant 2047094]. Supplemental Material: The online supplements are available at https://doi.org/10.1287/ijoc.2022.1226 . 

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2. Individualized Causal Discovery with Latent Trajectory Embedded Bayesian Networks

   https://doi.org/10.1111/biom.13843  

   Zhou, Fangting ; He, Kejun ; Ni, Yang ( February 2023 , Biometrics)

   Bayesian networks have been widely used to generate causal hypotheses from multivariate data. Despite their popularity, the vast majority of existing causal discovery approaches make the strong assumption of a (partially) homogeneous sampling scheme. However, such assumption can be seriously violated, causing significant biases when the underlying population is inherently heterogeneous. To this end, we propose a novel causal Bayesian network model, termed BN-LTE, that embeds heterogeneous samples onto a low-dimensional manifold and builds Bayesian networks conditional on the embedding. This new framework allows for more precise network inference by improving the estimation resolution from the population level to the observation level. Moreover, while causal Bayesian networks are in general not identifiable with purely observational, cross-sectional data due to Markov equivalence, with the blessing of causal effect heterogeneity, we prove that the proposed BN-LTE is uniquely identifiable under relatively mild assumptions. Through extensive experiments, we demonstrate the superior performance of BN-LTE in causal structure learning as well as inferring observation-specific gene regulatory networks from observational data.

    

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3. Prior and posterior checking of implicit causal assumptions

   https://doi.org/10.1111/biom.13886  

   Linero, Antonio R. ( June 2023 , Biometrics)

   Causal inference practitioners have increasingly adopted machine learning techniques with the aim of producing principled uncertainty quantification for causal effects while minimizing the risk of model misspecification. Bayesian nonparametric approaches have attracted attention as well, both for their flexibility and their promise of providing natural uncertainty quantification. Priors on high‐dimensional or nonparametric spaces, however, can often unintentionally encode prior information that is at odds with substantive knowledge in causal inference—specifically, the regularization required for high‐dimensional Bayesian models to work can indirectly imply that the magnitude of the confounding is negligible. In this paper, we explain this problem and provide tools for (i) verifying that the prior distribution does not encode an inductive bias away from confounded models and (ii) verifying that the posterior distribution contains sufficient information to overcome this issue if it exists. We provide a proof‐of‐concept on simulated data from a high‐dimensional probit‐ridge regression model, and illustrate on a Bayesian nonparametric decision tree ensemble applied to a large medical expenditure survey.

    

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4. A biologist's guide to model selection and causal inference

   https://doi.org/10.1098/rspb.2020.2815  

   Laubach, Zachary M. ; Murray, Eleanor J. ; Hoke, Kim L. ; Safran, Rebecca J. ; Perng, Wei ( January 2021 , Proceedings of the Royal Society B: Biological Sciences)

   A goal of many research programmes in biology is to extract meaningful insights from large, complex datasets. Researchers in ecology, evolution and behavior (EEB) often grapple with long-term, observational datasets from which they construct models to test causal hypotheses about biological processes. Similarly, epidemiologists analyse large, complex observational datasets to understand the distribution and determinants of human health. A key difference in the analytical workflows for these two distinct areas of biology is the delineation of data analysis tasks and explicit use of causal directed acyclic graphs (DAGs), widely adopted by epidemiologists. Here, we review the most recent causal inference literature and describe an analytical workflow that has direct applications for EEB. We start this commentary by defining four distinct analytical tasks (description, prediction, association, causal inference). The remainder of the text is dedicated to causal inference, specifically focusing on the use of DAGs to inform the modelling strategy. Given the increasing interest in causal inference and misperceptions regarding this task, we seek to facilitate an exchange of ideas between disciplinary silos and provide an analytical framework that is particularly relevant for making causal inference from observational data. 

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5. Understanding pretense as causal inference

   https://doi.org/10.1016/j.dr.2023.101065  

   Sobel, David M ( June 2023 , Developmental Review)

   There is a long-standing interest in the role that children’s understanding of pretense plays in their more general theory of mind development. Some argue that children understand pretense as a mental state, and the capacity to pretend is indicative of children possessing the capacity for mental representations. Others argue that children understand pretense in terms of actions and appearances, and an understanding of the mental states involved in pretending has a prolonged developmental trajectory. The goal of this paper is to integrate these ideas by positing that children understand pretense as a form of causal inference, which is based on both their general causal reasoning capacities and specific knowledge of mental states. I will first review literature on children’s understanding of pretense, and how such understanding can be conceptualized as integrating with children’s causal reasoning ability. I will then consider how children’s causal knowledge affects the ways they make inferences about others’ pretense. Next, I will consider the role of causal knowledge more broadly in children’s reasoning about pretense worlds, judgments of possibility, and counterfactual reasoning. Taken together the goal of this review is to synthesize how children understand pretending into a rational constructivist framework for understanding social cognitive development in a more integrative manner. 

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