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1. Unit Selection with Causal Diagram

   A. Li and J. Pearl (March 2022, Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22))

   Vasant Honavar and Matthijs Spaan (Ed.)

   The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if encouraged and a different way if not encouraged. Using a combination of experimental and observational data, Li and Pearl derived tight bounds on the “benefit function” - the payoff/cost associated with selecting an individual with given characteristics. This paper shows that these bounds can be narrowed significantly (enough to change decisions) when structural information is available in the form of a causal model. We address the problem of estimating the benefit function using observational and experimental data when specific graphical criteria are assumed to hold. 

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2. Unit Selection Based on Counterfactual Logic

   https://doi.org/10.24963/ijcai.2019/248  

   Li, Ang; Pearl, Judea (August 2019, Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence)

   The unit selection problem aims to identify a set of individuals who are most likely to exhibit a desired mode of behavior, which is defined in counterfactual terms. A typical example is that of selecting individuals who would respond one way if encouraged and a different way if not encouraged. Unlike previous works on this problem, which rely on ad-hoc heuristics, we approach this problem formally, using counterfactual logic, to properly capture the nature of the desired behavior. This formalism enables us to derive an informative selection criterion which integrates experimental and observational data. We demonstrate the superiority of this criterion over A/B-test-based approaches. 

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3. Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds

   Kallus, Nathan; Zhou, Angela (January 2019, Advances in neural information processing systems)

   Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to evaluate the fairness of such policies, we show that actually auditing their disparate impacts per standard observational metrics, such as true positive rates, is impossible since ground truths are unknown. Whether our data is experimental or observational, an individual's actual outcome under an intervention different than that received can never be known, only predicted based on features. We prove how we can nonetheless point-identify these quantities under the additional assumption of monotone treatment response, which may be reasonable in many applications. We further provide a sensitivity analysis for this assumption via sharp partial-identification bounds under violations of monotonicity of varying strengths. We show how to use our results to audit personalized interventions using partially-identified ROC and xROC curves and demonstrate this in a case study of a French job training dataset. 

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4. Reference frequency: what do speakers tend to talk about?

   Arnold, J. E.; Strangmann, I.; Hwang, H.; Zerkle, S (January 2018, UNC Language Processing Lab Technical Reports)

   he pronoun “they” can refer to an individual who identifies as nonbinary, but it also is commonly used as a plural pronoun. How do listeners identify whether “they” is being used in a singular or plural sense? Arnold, Mayo, & Dong (in press) report three experiments in that test the role of explicitly introducing gender identity via pronouns, e.g. “This is Alex, and they use they/them pronouns.” Participants read short stories like “Alex went running with Liz and they fell down.” Answers to “Who fell down” indicated whether participants interpreted they as Alex or Alex-and-Liz. Singular interpretations of they were more likely when participants hear an explicit statement that Alex uses they/them pronouns, and in supporting discourse contexts. This paper is a companion to the main article, and reports analyses of individual difference measures. Participants self-reported familiarity with individuals who identify as nonbinary, which was expected to increase singular interpretations, but mostly it did not. In experiment 2 we also measured print exposure, but we found that it did not affect interpretation of singular they. In short, we saw virtually no effects of individual difference predictors. 

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5. Crystallographic variant mapping using precession electron diffraction data

   https://doi.org/10.20517/microstructures.2023.17  

   H. Hansen, Marcus (January 2023, Microstructures)

   In this work, we developed three methods to map crystallographic variants of samples at the nanoscale by analyzing precession electron diffraction data using a high-temperature shape memory alloy and a VO2 thin film on sapphire as the model systems. The three methods are (I) a user-selecting-reference pattern approach, (II) an algorithm-selecting-reference-pattern approach, and (III) a k-means approach. In the first two approaches, Euclidean distance, Cosine, and Structural Similarity (SSIM) algorithms were assessed for the diffraction pattern similarity quantification. We demonstrated that the Euclidean distance and SSIM methods outperform the Cosine algorithm. We further revealed that the random noise in the diffraction data can dramatically affect similarity quantification. Denoising processes could improve the crystallographic mapping quality. With the three methods mentioned above, we were able to map the crystallographic variants in different materials systems, thus enabling fast variant number quantification and clear variant distribution visualization. The advantages and disadvantages of each approach are also discussed. We expect these methods to benefit researchers who work on martensitic materials, in which the variant information is critical to understand their properties and functionalities. 

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