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1. Estimating Causal Effects from Learned Causal Networks

   Raichev, Anna; Tian, Jin; Ihler, Alexander; Dechter, Rina (October 2024, {IOS} Press)

   The standard approach to answering an identifiable causaleffect query (e.g., P(Y |do(X)) given a causal diagram and observational data is to first generate an estimand, or probabilistic expression over the observable variables, which is then evaluated using the observational data. In this paper, we propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Then, efficient probabilistic graphical model (PGM) algorithms can be applied to the learned model to answer queries. Perhaps surprisingly, we show that this model completion learning approach can be more effective than estimand approaches, particularly for larger models in which the estimand expressions become computationally difficult. We illustrate our method’s potential using a benchmark collection of Bayesian networks and synthetically generated causal models 

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2. Causal Support: Modeling Causal Inferences with Visualizations

   Kale, Alex; Wu, Yifan; Hullman, Jessica (January 2022, IEEE transactions on visualization and computer graphics)

   Analysts often make visual causal inferences about possible data-generating models. However, visual analytics (VA) software tends to leave these models implicit in the mind of the analyst, which casts doubt on the statistical validity of informal visual “insights”. We formally evaluate the quality of causal inferences from visualizations by adopting causal support—a Bayesian cognition model that learns the probability of alternative causal explanations given some data—as a normative benchmark for causal inferences. We contribute two experiments assessing how well crowdworkers can detect (1) a treatment effect and (2) a confounding relationship. We find that chart users’ causal inferences tend to be insensitive to sample size such that they deviate from our normative benchmark. While interactively cross-filtering data in visualizations can improve sensitivity, on average users do not perform reliably better with common visualizations than they do with textual contingency tables. These experiments demonstrate the utility of causal support as an evaluation framework for inferences in VA and point to opportunities to make analysts’ mental models more explicit in VA software. 

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3. Modular Learning of Deep Causal Generative Models for High-dimensional Causal Inference

   Rahman, Md_Musfiqur; Kocaoglu, Murat (May 2024, Proceedings of the 41st International Conference on Machine Learning)
4. Analysis of Causal and Non-Causal Convolution Networks for Time Series Classification

   https://doi.org/10.1137/1.9781611978032.91  

   Saini, Uday Singh; Zhuang, Zhongfang; Yeh, Chin-Chia Michael; Zhang, Wei; Papalexakis, Evangelos E (January 2024, Society for Industrial and Applied Mathematics)

   Applications of neural networks like MLPs and ResNets in temporal data mining has led to improvements on the problem of time series classification. Recently, a new class of networks called Temporal Convolution Networks (TCNs) have been proposed for various time series tasks. Instead of time invariant convolutions they use temporally causal convolutions, this makes them more constrained than ResNets but surprisingly good at generalization. This raises an important question: How does a network with causal convolution solve these tasks when compared to a network with acausal convolutions? As the first attempt at answering these questions, we analyze different architectures through a lens of representational subspace similarity. We demonstrate that the evolution of input representations in the layers of TCNs is markedly different from ResNets and MLPs. We find that acausal networks are prone to form groupings of similar layers and TCNs on the other hand learn representations that are much more diverse throughout the network. Next, we study the convergence properties of internal layers across different architecture families and discover that the behaviour of layers inside Acausal network is more homogeneous when compared to TCNs. Our extensive empirical studies offer new insights into internal mechanisms of convolution networks in the domain of time series analysis and may assist practitioners gaining deeper understanding of each network. 

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5. Entropy of causal diamond ensembles

   https://doi.org/10.21468/SciPostPhys.15.1.023  

   Jacobson, Ted; Visser, Manus R (July 2023, SciPost Physics)

   We define a canonical ensemble for a gravitational causal diamond by introducing an artificial York boundary inside the diamond with a fixed induced metric and temperature, and evaluate the partition function using a saddle point approximation. For Einstein gravity with zero cosmological constant there is no exact saddle with a horizon, however the portion of the Euclidean diamond enclosed by the boundary arises as an approximate saddle in the high-temperature regime, in which the saddle horizon approaches the boundary. This high-temperature partition function provides a statistical interpretation of the recent calculation of Banks, Draper and Farkas, in which the entropy of causal diamonds is recovered from a boundary term in the on-shell Euclidean action. In contrast, with a positive cosmological constant, as well as in Jackiw-Teitelboim gravity with or without a cosmological constant, an exact saddle exists with a finite boundary temperature, but in these cases the causal diamond is determined by the saddle rather than being selected a priori. 

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