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1. Don't throw the associative baby out with the Bayesian bathwater: Children are more associative when reasoning retrospectively under information processing demands

   https://doi.org/10.1111/desc.13464  

   Benton, Deon T; Kamper, David; Beaton, Rebecca M; Sobel, David M (May 2024, Developmental Science)

   Abstract Causal reasoning is a fundamental cognitive ability that enables individuals to learn about the complex interactions in the world around them. However, the mechanisms that underpin causal reasoning are not well understood. For example, it remains unresolved whether children's causal inferences are best explained by Bayesian inference or associative learning. The two experiments and computational models reported here were designed to examine whether 5‐ and 6‐year‐olds will retrospectively reevaluate objects—that is, adjust their beliefs about the causal status of some objects presented at an earlier point in time based on the observed causal status of other objects presented at a later point in time—when asked to reason about 3 and 4 objects and under varying degrees of information processing demands. Additionally, the experiments and models were designed to determine whether children's retrospective reevaluations were best explained by associative learning, Bayesian inference, or some combination of both. The results indicated that participants retrospectively reevaluated causal inferences under minimal information‐processing demands (Experiment 1) but failed to do so under greater information processing demands (Experiment 2) and that their performance was better captured by an associative learning mechanism, with less support for descriptions that rely on Bayesian inference. Research HighlightsFive‐ and 6‐year‐old children engage in retrospective reevaluation under minimal information‐processing demands (Experiment 1).Five‐ and 6‐year‐old children do not engage in retrospective reevaluation under more extensive information‐processing demands (Experiment 2).Across both experiments, children's retrospective reevaluations were better explained by a simple associative learning model, with only minimal support for a simple Bayesian model.These data contribute to our understanding of the cognitive mechanisms by which children make causal judgements. 

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2. Proximity Search in the Greedy Tree

   https://doi.org/10.1137/1.9781611977585.ch29  

   Chubet, Oliver; Parikh, Parth; Sheehy, Donald R.; Sheth, Siddharth (January 2023, SIAM Symposium on Simplicity in Algorithms)

   Telikepalli Kavitha; Kurt Mehlhorn (Ed.)

   Over the last 50 years, there have been many data structures proposed to perform proximity search problems on metric data. Perhaps the simplest of these is the ball tree, which was independently discovered multiple times over the years. However, there is a lack of strong theoretical guarantees for standard ball trees, often leading to more complicated structures when guarantees are required. In this paper, we present the greedy tree, a simple ball tree construction for which we can prove strong theoretical guarantees for proximity search queries, matching the state of the art under reasonable assumptions. To our knowledge, this is the first ball tree construction providing such guarantees. Like a standard ball tree, it is a binary tree with the points stored in the leaves. Only a point, a radius, and an integer are stored for each node. The asymptotic running times of search algorithms in the greedy tree match those of more complicated structures regularly used in practice. 

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3. Using Survival Information in Truncation by Death Problems without the Monotonicity Assumption

   https://doi.org/10.1111/biom.12883  

   Yang, Fan; Ding, Peng (April 2018, Biometrics)

   Summary In some randomized clinical trials, patients may die before the measurement time point of their outcomes. Even though randomization generates comparable treatment and control groups, the remaining survivors often differ significantly in background variables that are prognostic to the outcomes. This is called the truncation by death problem. Under the potential outcomes framework, the only well-defined causal effect on the outcome is within the subgroup of patients who would always survive under both treatment and control. Because the definition of the subgroup depends on the potential values of the survival status that could not be observed jointly, without making strong parametric assumptions, we cannot identify the causal effect of interest and consequently can only obtain bounds of it. Unfortunately, however, many bounds are too wide to be useful. We propose to use detailed survival information before and after the measurement time point of the outcomes to sharpen the bounds of the subgroup causal effect. Because survival times contain useful information about the final outcome, carefully utilizing them could improve statistical inference without imposing strong parametric assumptions. Moreover, we propose to use a copula model to relax the commonly-invoked but often doubtful monotonicity assumption that the treatment extends the survival time for all patients. 

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4. Detecting confounders in multivariate time series using strength of causation

   Y. Liu, C (January 2023, EUSIPCO)

   One of the most important problems in science is understanding causation. This problem is particularly challenging when causation has to be inferred from observational data only. A further challenge of this problem is if the observed data were generated in the presence of latent confounders. In this paper, we propose a method for detecting confounders in multivariate time series using a recently introduced concept referred to as differential causal effect (DCE). The solution is based on feature-based Gaussian processes that are not only used for estimating the DCE of the observed time series but also for estimating the latent confounders. We demonstrate the performance of the proposed method with several examples. They show that the proposed approach can detect confounders and can accurately estimate causal strengths. 

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5. Privacy-Aware Rejection Sampling

   Awan, Jordan; Rao, Vinayak (February 2023, Journal of machine learning research)

   Weller, Adrian (Ed.)

   While differential privacy (DP) offers strong theoretical privacy guarantees, implementations of DP mechanisms may be vulnerable to side-channel attacks, such as timing attacks. When sampling methods such as MCMC or rejection sampling are used to implement a privacy mechanism, the runtime can leak private information. We characterize the additional privacy cost due to the runtime of a rejection sampler in terms of both (, δ)-DP as well as f -DP. We also show that unless the acceptance probability is constant across databases, the runtime of a rejection sampler does not satisfy -DP for any . We show that there is a similar breakdown in privacy with adaptive rejection samplers. We propose three modifications to the rejection sampling algorithm, with varying assumptions, to protect against timing attacks by making the runtime independent of the data. The modification with the weakest assumptions is an approximate sampler, introducing a small increase in the privacy cost, whereas the other modifications give perfect samplers. We also use our techniques to develop an adaptive rejection sampler for log-H ̈older densities, which also has data-independent runtime. We give several examples of DP mechanisms that fit the assumptions of our methods and can thus be implemented using our samplers. 

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