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1. Learning causality with graphs

   https://doi.org/10.1002/aaai.12070  

   Ma, Jing; Li, Jundong (December 2022, AI Magazine)

   Abstract Recent years have witnessed a rocketing growth of machine learning methods on graph data, especially those powered by effective neural networks. Despite their success in different real‐world scenarios, the majority of these methods on graphs only focus on predictive or descriptive tasks, but lack consideration of causality. Causal inference can reveal the causality inside data, promote human understanding of the learning process and model prediction, and serve as a significant component of artificial intelligence (AI). An important problem in causal inference is causal effect estimation, which aims to estimate the causal effects of a certain treatment (e.g., prescription of medicine) on an outcome (e.g., cure of disease) at an individual level (e.g., each patient) or a population level (e.g., a group of patients). In this paper, we introduce the background of causal effect estimation from observational data, envision the challenges of causal effect estimation with graphs, and then summarize representative approaches of causal effect estimation with graphs in recent years. Furthermore, we provide some insights for future research directions in related area. Link to video abstract:https://youtu.be/BpDPOOqw‐ns 

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2. Parallel Gradient Boosting based Granger Causality Learning

   https://doi.org/10.1109/BigData47090.2019.9005690  

   Guo, Pei; Liu, Chen; Tang, Yan; Wang, Jianwu (December 2019, 2019 IEEE International Conference on Big Data (Big Data))

   Granger causality and its learning algorithms have been widely used in many disciplines to study cause-effect relationship among time series variables. In this paper, we address computing challenges of state-of-art Granger causality learning algorithms, specially when facing increasing dimensionality of available datasets. We study how to leverage gradient boosting meta machine learning techniques to achieve accurate causality discovery and big data parallel techniques for efficient causality discovery from large temporal datasets. We propose two main algorithms for gradient boosting based causality learning, and parallel gradient boosting based causality learning. Our experiments show our proposed algorithms can achieve efficient learning in distributed environments with good learning accuracy. 

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3. Silva: Interactively Assessing Machine Learning Fairness Using Causality

   https://doi.org/10.1145/3313831.3376447  

   Yan, Jing Nathan; Gu, Ziwei; Lin, Hubert; Rzeszotarski, Jeffrey M. (April 2020, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

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4. Causality Enhanced Societal Event Forecasting With Heterogeneous Graph Learning

   https://doi.org/10.1109/ICDM54844.2022.00019  

   Deng, Songgaojun; Rangwala, Huzefa; Ning, Yue (November 2022, 2022 IEEE International Conference on Data Mining (ICDM))
5. Learning From Strategic Agents: Accuracy, Improvement, and Causality, ICML

   Shavit, Yonadav; Edelman, Benjamin; Axelrod, Brian (July 2020, Proceedings of Machine Learning Research)

   In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' incentives to ``game'' their features in order to receive better decisions. Whereas the strategic classification literature generally assumes that agents' outcomes are not causally dependent on their features (and thus strategic behavior is a form of lying), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. Our formulation is the first to incorporate a crucial phenomenon: when agents act to change observable features, they may as a side effect perturb unobserved features that causally affect their true outcomes. We consider three distinct desiderata for a decision-maker's model: accurately predicting agents' post-gaming outcomes (accuracy), incentivizing agents to improve these outcomes (improvement), and, in the linear setting, estimating the visible coefficients of the true causal model (causal precision). As our main contribution, we provide the first algorithms for learning accuracy-optimizing, improvement-optimizing, and causal-precision-optimizing linear regression models directly from data, without prior knowledge of agents' possible actions. These algorithms circumvent the hardness result of Miller et al. (2019) by allowing the decision maker to observe agents' responses to a sequence of decision rules, in effect inducing agents to perform causal interventions for free. 

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