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1. FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference

   Wang, Tianyu; Morucci, Marco; Awan, M. Usaid; Liu, Yameng; Roy, Sudeepa; Rudin, Cynthia; Volfovsky, Alexander (January 2021, Journal of machine learning research)

   null (Ed.)

   A classical problem in causal inference is that of matching, where treatment units need to be matched to control units based on covariate information. In this work, we propose a method that computes high quality almost-exact matches for high-dimensional categorical datasets. This method, called FLAME (Fast Large-scale Almost Matching Exactly), learns a distance metric for matching using a hold-out training data set. In order to perform matching efficiently for large datasets, FLAME leverages techniques that are natural for query processing in the area of database management, and two implementations of FLAME are provided: the first uses SQL queries and the second uses bit-vector techniques. The algorithm starts by constructing matches of the highest quality (exact matches on all covariates), and successively eliminates variables in order to match exactly on as many variables as possible, while still maintaining interpretable high-quality matches and balance between treatment and control groups. We leverage these high quality matches to estimate conditional average treatment effects (CATEs). Our experiments show that FLAME scales to huge datasets with millions of observations where existing state-of-the-art methods fail, and that it achieves significantly better performance than other matching methods. 

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2. Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

   Awan, Usaid; Morucci, Marco; Orlandi, Vittorio; Roy, Sudeepa; Rudin, Cynthia; Volfovsky, Alexander (January 2020, Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics)

   null (Ed.)

   We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others’ outcomes. Traditional treatment effect estimators for randomized experiments are biased and error prone in this setting. Our method matches units almost exactly on counts of unique subgraphs within their neighborhood graphs. The matches that we construct are interpretable and high-quality. Our method can be extended easily to accommodate additional unit-level covariate information. We show empirically that our method performs better than other existing methodologies for this problem, while producing meaningful, interpretable results. 

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3. Regularizing Black-box Models for Improved Interpretability

   Gregory Plumb, Maruan Al-Shedivat (December 2020, NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems)

   Most of the work on interpretable machine learning has focused on designing either inherently interpretable models, which typically trade-off accuracy for interpretability, or post-hoc explanation systems, whose explanation quality can be unpredictable. Our method, ExpO, is a hybridization of these approaches that regularizes a model for explanation quality at training time. Importantly, these regularizers are differentiable, model agnostic, and require no domain knowledge to define. We demonstrate that post-hoc explanations for ExpO-regularized models have better explanation quality, as measured by the common fidelity and stability metrics. We verify that improving these metrics leads to significantly more useful explanations with a user study on a realistic task. 

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4. Cultural adaptation congruence in immigrant spouses is associated with marital quality.

   https://doi.org/https://doi.org/10.1111/jomf.12799  

   Lester Sim, Shanting Chen (January 2021, Journal of marriage and family)

   null (Ed.)

   Objective and background Previous research suggests that cultural adaptation is associated with Mexican-origin couples' marital outcomes, including marital distress and rates of dissolution. However, research on the marital implications of different types of spousal differences in cultural adaptation often omits important dyadic dynamics (i.e., incongruence between couples and with their partners); this, coupled with existing methodological issues, might contribute to the pattern of mixed findings in the literature. Method Using data from 273 Mexican-origin couples, we conducted response surface analyses to examine how spousal congruence in four adaptation domains (acculturation, enculturation, English proficiency, Spanish proficiency) is associated with wives' and husbands' marital warmth, hostility and satisfaction. Results Higher, versus lower, levels of couple matches (except for enculturation) were associated with better marital quality. Mismatches in American (acculturation, English) and Mexican (enculturation, Spanish) orientations were also associated with higher, and lower, marital quality, respectively. Conclusion and implication Our findings highlight the importance of examining couple matching, which has historically been understudied. We also suggest that inconsistencies in prior work can be explained by discrepant associations between mismatches in American versus Mexican orientation and relationship outcomes. 

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5. The Segmentation-Thickness Tradeoff in Online Marketplaces

   https://doi.org/10.1145/3311089  

   Alijani, Reza; Banerjee, Siddhartha; Gollapudi, Sreenivas; Kollias, Kostas; Munagala, Kamesh (March 2019, ACM SIGMETRICS performance evaluation review)

   A core tension in the operations of online marketplaces is between segmentation (wherein platforms can increase revenue by segmenting the market into ever smaller sub-markets) and thickness (wherein the size of the sub-market affects the utility experienced by an agent). An important example of this is in dynamic online marketplaces, where buyers and sellers, in addition to preferences for different matches, also have finite patience (or deadlines) for being matched. We formalize this trade-off via a novel optimization problem that we term as 'Two-sided Facility Location': we consider a market wherein agents arrive at nodes embedded in an underlying metric space, where the distance between a buyer and seller captures the quality of the corresponding match. The platform posts prices and wages at the nodes, and opens a set of virtual clearinghouses where agents are routed for matching. To ensure high match-quality, the platform imposes a distance constraint between an agent and its clearinghouse; to ensure thickness, the platform requires the flow to any clearinghouse be at least a pre-specified lower bound. Subject to these constraints, the goal of the platform is to maximize the social surplus subject to weak budget balance, i.e., profit being non-negative. Our work characterizes the complexity of this problem by providing both hardness results as well as algorithms for this setting; in particular, we present an algorithm that for any constant ε > 0 yields a (1 + ε ) approximation for the gains from trade, while relaxing the match quality (i.e., maximum distance of any match) by a constant factor. 

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