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1. Intervention with limited information

   https://doi.org/10.1007/s00182-021-00796-8  

   Fudenberg, Drew; Levine, David K. (October 2021, International Journal of Game Theory)
2. Efficient Algorithms towards Network Intervention

   https://doi.org/10.1145/3366423.3380269  

   Hung, Hui-Ju; Lee, Wang-Chien; Yang, De-Nian; Shen, Chih-Ya; Lei, Zhen; Chow, Sy-Miin (April 2020, Proceedings of The Web Conference 2020)

   Research suggests that social relationships have substantial impacts on individuals’ health outcomes. Network intervention, through careful planning, can assist a network of users to build healthy relationships. However, most previous work is not designed to assist such planning by carefully examining and improving multiple network characteristics. In this paper, we propose and evaluate algorithms that facilitate network intervention planning through simultaneous optimization of network degree, closeness, betweenness, and local clustering coefficient, under scenarios involving Network Intervention with Limited Degradation - for Single target (NILD-S) and Network Intervention with Limited Degradation - for Multiple targets (NILD-M). We prove that NILD-S and NILD-M are NP-hard and cannot be approximated within any ratio in polynomial time unless P=NP. We propose the Candidate Re-selection with Preserved Dependency (CRPD) algorithm for NILD-S, and the Objective-aware Intervention edge Selection and Adjustment (OISA) algorithm for NILD-M. Various pruning strategies are designed to boost the efficiency of the proposed algorithms. Extensive experiments on various real social networks collected from public schools and Web and an empirical study are conducted to show that CRPD and OISA outperform the baselines in both efficiency and effectiveness. 

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3. Identifying Representations for Intervention Extrapolation

   Saengkyongam, Sorawit; Rosenfeld, Elan; Ravikumar, Pradeep; Pfister, Niklas; Peters, Jonas (May 2024, International Conference on Learning Representations (ICLR) 2024)

   The premise of identifiable and causal representation learning is to improve the current representation learning paradigm in terms of generalizability or robustness. Despite recent progress in questions of identifiability, more theoretical results demonstrating concrete advantages of these methods for downstream tasks are needed. In this paper, we consider the task of intervention extrapolation: predicting how interventions affect an outcome, even when those interventions are not observed at training time, and show that identifiable representations can provide an effective solution to this task even if the interventions affect the outcome non-linearly. Our setup includes an outcome variable Y , observed features X, which are generated as a non-linear transformation of latent features Z, and exogenous action variables A, which influence Z. The objective of intervention extrapolation is then to predict how interventions on A that lie outside the training support of A affect Y . Here, extrapolation becomes possible if the effect of A on Z is linear and the residual when regressing Z on A has full support. As Z is latent, we combine the task of intervention extrapolation with identifiable representation learning, which we call Rep4Ex: we aim to map the observed features X into a subspace that allows for non-linear extrapolation in A. We show that the hidden representation is identifiable up to an affine transformation in Z-space, which, we prove, is sufficient for intervention extrapolation. The identifiability is characterized by a novel constraint describing the linearity assumption of A on Z. Based on this insight, we propose a flexible method that enforces the linear invariance constraint and can be combined with any type of autoencoder. We validate our theoretical findings through a series of synthetic experiments and show that our approach can indeed succeed in predicting the effects of unseen interventions. 

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4. PEDI-Piazza Explorer Dashboard for Intervention

   Akintunde, Ruth Okoilu; Limke, Ally; Barnes, Tiffany; Heckman, Sarah; Lynch, Collin (April 2021, 2021 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC))

   Harms, Kyle; Cunha, Jácome; Oney, Steve; Kelleher, Caitlin (Ed.)

   Analytics about how students navigate online learning tools throughout the duration of an assignment is scarce. Knowledge about how students use online tools before a course’s end could positively impact students’ learning outcomes. We introduce PEDI (Piazza Explorer Dashboard for Intervention), a tool which analyzes and presents visualizations of forum activity on Piazza, a question and answer forum, to instructors. We outline the design principles and data-informed recommendations used to design PEDI. Our prior research revealed two critical periods in students’ forum engagement over the duration of an assignment. Early engagement in the first half of an assignment duration positively correlates with class average performance. Whereas, extremely high engagement toward the deadline predicted lower class average performance. PEDI uses these findings to detect and flag troubling engagement levels and informs instructors through clear visualizations to promote data-informed interventions. By providing insights to instructors, PEDI may improve class performance and pave the way for a new generation of online tools. 

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5. Causal Graph Identification Under Soft Intervention

   Peng, Chen; Mitra, Urbashi (June 2025, IEEE)