The impact of social health insurance on rural populations by Green, C, Hollingsworth, B, Yang, M

“…Given the voluntary nature of the insurance enrolment, we exploit the uneven roll-out of the programme across rural counties as a natural experiment to explore causal inference. We find little effect of the insurance on the use of formal medical care and out-of-pocket health payments. …”

Winner–loser plant trait replacements in human-modified tropical forests by Pinho, BX, Melo, FPL, ter Braak, CJF, Bauman, D, Maréchaux, I, Tabarelli, M, Benchimol, M, Arroyo-Rodriguez, V, Santos, BA, Hawes, JE, Berenguer, E, Ferreira, J, Silveira, JM, Peres, CA, Rocha‐Santos, L, Souza, FC, Gonçalves-Souza, T, Mariano-Neto, E, Faria, D, Barlow, J

“…Here we investigate mechanisms and functional consequences of this winner–loser replacement in six human-modified Amazonian and Atlantic Forest regions in Brazil using a causal inference framework. Combining floristic and functional trait data for 1,207 tree species across 271 forest plots, we find that forest loss consistently caused an increased dominance of low-density woods and small seeds dispersed by endozoochory (winner traits) and the loss of distinctive traits, such as extremely dense woods and large seeds dispersed by synzoochory (loser traits). …”

Cross-correlation between auditory and visual signals promotes multisensory integration. by Parise, C, Harrar, V, Ernst, M, Spence, C

“…Whether multiple signals have a common origin or not must, however, be inferred from the signals themselves through a causal inference process. Recent studies have demonstrated that cross-correlation, that is, the similarity in temporal structure between unimodal signals, represents a powerful cue for solving the correspondence problem in humans. …”

Motion modelling using concepts of fuzzy artificial potential fields by Motlagh, Omid Reza Esmaeili, Ramli, Abdul Rahman, Tang, Sai Hong, Motlagh, Farid Esmaeili, Ismail, Napsiah

“…In previous work, the authors described a reliable motion modelling technique using causal inference of fuzzy cognitive maps (FCM) which has been efficiently modified for the purpose of this contribution. …”
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Randomization Inference and Sensitivity Analysis for Composite Null Hypotheses With Binary Outcomes in Matched Observational Studies by Shi, Pixu, Mikkelsen, Mark E., Small, Dylan S., Fogarty, Colin B

“…Supplementary materials for this article are available online. Keywords: Causal inference; Causal risk; Effect ratio; Integer programming; Sensitivity analysis…”
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Causal structure discovery from incomplete data by Squires, Chandler(Chandler B.)

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Design, Identification, and Sensitivity Analysis for Patient Preference Trials

“…In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. …”
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High-dimensional joint estimation of multiple directed Gaussian graphical models by Wang, Yuhao, Segarra, Santiago, Uhler, Caroline

“…As a corollary, we also obtain high-dimensional consistency results for causal inference from a mix of observational and interventional data. …”
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Uncertainty Quantification in Deep Learning Models of G-Computation for Outcome Prediction under Dynamic Treatment Regimes by Deng, Leon

“…G-Net is a neural network framework that implements g-computation, a causal inference method for making counterfactual predictions and estimating treatment effects under dynamic and time-varying treatment regimes. …”
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Visual relationship detection by Lee, Xavier Eugene

“…This paper presents a SGG framework with the novel Total Direct Effect (TDE) analysis within causal inference. The proposed framework is compared against a conventional causal effect framework: SGG framework with Total Effect (TE) analysis. …”
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Signal in slaughter: endogenous conflict dynamics and violence against civilians in the 1948 war by Haran, A

“…I develop these assertions with formal and computational modelling, and test the resulting predictions with the combination of statistical analysis of original fine-grained data on the Palestinian exodus and numerous qualitative case studies, aiming at causal inference. The dissertation contributes to our understanding of violence against civilians in armed conflict and to the salient debate over the events of the 1948 war.…”

Exploring the links between colours and tastes/flavours by Spence, C, Levitan, CA

“…It is currently unclear to what extent such colour-taste/flavour correspondences ought to be explained in terms of semantic congruency (i.e., statistical learning), and/or emotional mediation. Bayesian causal inference has become an increasingly important tool in helping researchers to understand (and predict) the multisensory interactions between the spatial senses of vision, audition, and touch. …”

Social network analysis and agent-based modeling in social epidemiology. by El-Sayed, A, Scarborough, P, Seemann, L, Galea, S

“…However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. …”

A brief review of hypernetworks in deep learning by Chauhan, VK, Zhou, J, Lu, P, Molaei, S, Clifton, DA

“…Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. …”

A brief review of hypernetworks in deep learning by Chauhan, VK, Molaei, S, Clifton, DA, Lu, P, Zhou, J

“…Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning etc. …”

Making sense of the evidence in population health intervention research: building a dry stone wall by Ogilvie, D, Bauman, A, Foley, L, Guell, C, Humphreys, D, Panter, J

“…In this way we might channel a spirit of pragmatic pluralism into making sense of complex sets of evidence, robust enough to support more plausible causal inference to guide action, while accepting and adapting to the reality of the public health landscape rather than wishing it were otherwise. …”

Examining overweight and obesity as risk factors for common mental disorders using fat mass and obesity-associated (FTO) genotype-instrumented analysis: The Whitehall II Study, 198... by Kivimäki, M, Jokela, M, Hamer, M, Geddes, J, Ebmeier, K, Kumari, M, Singh-Manoux, A, Hingorani, A, Batty, G

“…The Mendelian randomization approach exploits genetic variants to improve causal inference when using observational data. The authors examined the relation between long-term obesity and common mental disorders (CMD) by utilizing the known relation between fat mass and obesity-associated (FTO) genotype and body mass index (BMI; weight (kg)/height (m)(2)). …”

A brief review of hypernetworks in deep learning by Chauhan, VK, Zhou, J, Lu, P, Molaei, S, Clifton, DA

“…Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. …”

Design, Identification, and Sensitivity Analysis for Patient Preference Trials by Knox, Dean, Yamamoto, Teppei, Baum, Matthew A., Berinsky, Adam

“…In this paper, we provide a systematic analysis of PPTs based on the potential outcomes framework of causal inference. We propose a general design for PPTs with multi-valued treatments, where participants state their preferred treatments and are then randomized into either a standard RCT or a self-selection condition. …”
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Bias and High-Dimensional Adjustment in Observational Studies of Peer Effects by Eckles, Dean, Bakshy, Eytan

“…More generally, these results show how large, high-dimensional datasets and statistical learning can be used to improve causal inference. Supplementary materials for this article are available online.…”
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