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1. Combining the causal judgments of experts with possibly different focus areas

   Friedenberg, Meir; Halpern, Joseph Y. (October 2018, Proceedings)

   In many real-world settings, a decision-maker must combine information provided by different experts in order to decide on an effective policy. Alrajeh, Chockler, and Halpern showed how to combine causal models that are compatible in the sense that, for variables that appear in both models, the experts agree on the causal structure. In this work we show how causal models can be combined in cases where the experts might disagree on the causal structure for variables that appear in both models due to having different focus areas. We provide a new formal definition of compatibility of models in this setting and show how compatible models can be combined. We also consider the complexity of determining whether models are compatible. We believe that the notions defined in this work are of direct relevance to many practical decision making scenarios that come up in natural, social, and medical science settings. 

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2. Causal Learning with Interrupted Time Series

   Zhang, Yiwen; Rottman, Benjamin (January 2021, Proceedings of the Annual Conference of the Cognitive Science Society)

   Fitch, T; Lamm, C; Leder, H; Tessmar, K (Ed.)

   Interrupted time series analysis (ITSA) is a statistical procedure that evaluates whether an intervention causes a change in the intercept and/or slope of the time series. However, very little research has accessed causal learning in interrupted time series situations. We systematically investigated whether people are able to learn causal influences from a process akin to ITSA, and compared four different presentation formats of stimuli. We found that participants’ judgments agreed with ITSA in cases in which the pre-intervention slope is zero or in the same direction as the changes in intercept or slope. How- ever, participants had considerable difficulty controlling for pre-intervention slope when it is in the opposite direction of the changes in intercept or slope. The presentation formats didn’t affect judgments in most cases, but did in one. We discuss these results in terms of two potential heuristics that people might use aside from a process akin to ITSA. 

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3. Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanisms

   https://doi.org/10.24963/ijcai.2024/476  

   Komanduri, Aneesh; Wu, Yongkai; Chen, Feng; Wu, Xintao (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   Learning disentangled causal representations is a challenging problem that has gained significant attention recently due to its implications for extracting meaningful information for downstream tasks. In this work, we define a new notion of causal disentanglement from the perspective of independent causal mechanisms. We propose ICM-VAE, a framework for learning causally disentangled representations supervised by causally related observed labels. We model causal mechanisms using nonlinear learnable flow-based diffeomorphic functions to map noise variables to latent causal variables. Further, to promote the disentanglement of causal factors, we propose a causal disentanglement prior learned from auxiliary labels and the latent causal structure. We theoretically show the identifiability of causal factors and mechanisms up to permutation and elementwise reparameterization. We empirically demonstrate that our framework induces highly disentangled causal factors, improves interventional robustness, and is compatible with counterfactual generation. 

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4. A causal analysis of harm, \emph{Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022)}, 2022 (with S. Beckers and H. Chockler).

   S. Beckers; H. Chockler; J. Y. Halpern (November 2022, Proceedings of the 36th Conference on Neural Information Processing Systems (NeurIPS))

   As autonomous systems rapidly become ubiquitous, there is a growing need for a legal and regulatory framework that addresses when and how such a system harms someone. There have been several attempts within the philosophy literature to define harm, but none of them has proven capable of dealing with the many examples that have been presented, leading some to suggest that the notion of harm should be abandoned and ``replaced by more well-behaved notions''. As harm is generally something that is caused, most of these definitions have involved causality at some level. Yet surprisingly, none of them makes use of causal models and the definitions of actual causality that they can express. In this paper we formally define a qualitative notion of harm that uses causal models and is based on a well-known definition of actual causality due to Halpern and Pearl. The key features of our definition are that it is based on contrastive causation and uses a default utility to which the utility of actual outcomes is compared. We show that our definition is able to handle the examples from the literature, and illustrate its importance for reasoning about situations involving autonomous systems. 

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5. Abstracting causal models

   Beckers, Sander; Halpern, Joseph Y. (January 2019, Abstracting causal models, Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence)

   We consider a sequence of successively more restrictive definitions of abstraction for causal models, starting with a notion introduced by Rubenstein et al. called exact transformation that applies to probabilistic causal models, moving to a notion of uniform transformation that applies to deterministic causal models and does not allow differences to be hidden by the ``right'' choice of distribution, and then to abstraction, where the interventions of interest are determined by the map from low-level states to high-level states, and strong abstraction, which takes more seriously all potential interventions in a model, not just the allowed interventions. We show that procedures for combining micro-variables into macro-variables are instances of our notion of strong abstraction, as are all the examples considered by Rubenstein et al. 

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