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1. Towards robust relational causal discovery

   Lee, Sanghack ; Honavar, Vasant G. ( January 2019 , Conference on Uncertainty in Artificial Intelligence)

   We consider the problem of learning causal re- lationships from relational data. Existing ap- proaches rely on queries to a relational condi- tional independence (RCI) oracle to establish and orient causal relations in such a setting. In practice, queries to a RCI oracle have to be replaced by reliable tests for RCI against available data. Relational data present several unique challenges in testing for RCI. We study the conditions under which traditional iid-based CI tests yield reliable answers to RCI queries against relational data. We show how to conduct CI tests against relational data to robustly recover the underlying relational causal struc- ture. Results of our experiments demonstrate the effectiveness of our proposed approach. 

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2. Towards Conditional Independence Test for Relational Data

   Lee, Sanghack ; Honavar, Vasant G. ( January 2017 , Uncertainty in artificial intelligence)

   Conditional independence (CI) tests play a central role in statistical inference, machine learning, and causal discovery. Most existing CI tests assume that the samples are indepen- dently and identically distributed (i.i.d.). How- ever, this assumption often does not hold in the case of relational data. We define Relational Conditional Independence (RCI), a generaliza- tion of CI to the relational setting. We show how, under a set of structural assumptions, we can test for RCI by reducing the task of test- ing for RCI on non-i.i.d. data to the problem of testing for CI on several data sets each of which consists of i.i.d. samples. We develop Kernel Relational CI test (KRCIT), a nonpara- metric test as a practical approach to testing for RCI by relaxing the structural assumptions used in our analysis of RCI. We describe re- sults of experiments with synthetic relational data that show the benefits of KRCIT relative to traditional CI tests that don’t account for the non-i.i.d. nature of relational data. 

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3. Causal Relational Learning

   https://doi.org/10.1145/3318464.3389759  

   Salimi, Babak ; Parikh, Harsh ; Kayali, Moe ; Getoor, Lise ; Roy, Sudeepa ; Suciu, Dan ( May 2020 , SIGMOD)

   Causal inference is at the heart of empirical research in natu- ral and social sciences and is critical for scientific discovery and informed decision making. The gold standard in causal inference is performing randomized controlled trials; unfortu- nately these are not always feasible due to ethical, legal, or cost constraints. As an alternative, methodologies for causal inference from observational data have been developed in sta- tistical studies and social sciences. However, existing meth- ods critically rely on restrictive assumptions such as the study population consisting of homogeneous elements that can be represented in a single flat table, where each row is referred to as a unit. In contrast, in many real-world set- tings, the study domain naturally consists of heterogeneous elements with complex relational structure, where the data is naturally represented in multiple related tables. In this paper, we present a formal framework for causal inference from such relational data. We propose a declarative language called CaRL for capturing causal background knowledge and assumptions, and specifying causal queries using simple Datalog-like rules. CaRL provides a foundation for infer- ring causality and reasoning about the effect of complex interventions in relational domains. We present an extensive experimental evaluation on real relational data to illustrate the applicability of CaRL in social sciences and healthcare. 

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4. Preserved visual memory and relational cognition performance in monkeys with selective hippocampal lesions

   https://doi.org/10.1126/sciadv.aaz0484  

   Basile, Benjamin M. ; Templer, Victoria L. ; Gazes, Regina Paxton ; Hampton, Robert R. ( July 2020 , Science Advances)

   null (Ed.)

   The theory that the hippocampus is critical for visual memory and relational cognition has been challenged by discovery of more spared hippocampal tissue than previously reported in H.M., previously unreported extra-hippocampal damage in developmental amnesiacs, and findings that the hippocampus is unnecessary for object-in-context memory in monkeys. These challenges highlight the need for causal tests of hippocampal function in nonhuman primate models. Here, we tested rhesus monkeys on a battery of cognitive tasks including transitive inference, temporal order memory, shape recall, source memory, and image recognition. Contrary to predictions, we observed no robust impairments in memory or relational cognition either within- or between-groups following hippocampal damage. These results caution against over-generalizing from human correlational studies or rodent experimental studies, compel a new generation of nonhuman primate studies, and indicate that we should reassess the relative contributions of the hippocampus proper compared to other regions in visual memory and relational cognition. 

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5. GRASP: a goodness-of-fit test for classification learning

   https://doi.org/10.1093/jrsssb/qkad106  

   Javanmard, Adel ; Mehrabi, Mohammad ( September 2023 , Journal of the Royal Statistical Society Series B: Statistical Methodology)

   Performance of classifiers is often measured in terms of average accuracy on test data. Despite being a standard measure, average accuracy fails in characterising the fit of the model to the underlying conditional law of labels given the features vector (Y∣X), e.g. due to model misspecification, over fitting, and high-dimensionality. In this paper, we consider the fundamental problem of assessing the goodness-of-fit for a general binary classifier. Our framework does not make any parametric assumption on the conditional law Y∣X and treats that as a black-box oracle model which can be accessed only through queries. We formulate the goodness-of-fit assessment problem as a tolerance hypothesis testing of the form H0:E[Df(Bern(η(X))‖Bern(η^(X)))]≤τ where Df represents an f-divergence function, and η(x), η^(x), respectively, denote the true and an estimate likelihood for a feature vector x admitting a positive label. We propose a novel test, called Goodness-of-fit with Randomisation and Scoring Procedure (GRASP) for testing H0, which works in finite sample settings, no matter the features (distribution-free). We also propose model-X GRASP designed for model-X settings where the joint distribution of the features vector is known. Model-X GRASP uses this distributional information to achieve better power. We evaluate the performance of our tests through extensive numerical experiments.

    

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