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1. Welfare Effects of Ex-Ante Bias and Tie-Breaking Rules on Observational Learning with Fake Agents

   https://doi.org/10.23919/WiOpt58741.2023.10349844  

   Poojary, Pawan; Berry, Randall (August 2023, IEEE)

   Networks that provide agents with access to a common database of the agents' actions enable an agent to easily learn by observing the actions of others, but are also susceptible to manipulation by “fake” agents. Prior work has studied a model for the impact of such fake agents on ordinary (rational) agents in a sequential Bayesian observational learning framework. That model assumes that ordinary agents do not have an ex-ante bias in their actions and that they follow their private information in case of an ex-post tie between actions. This paper builds on that work to study the effect of fake agents on the welfare obtained by ordinary agents under different ex-ante biases and different tie-breaking rules. We show that varying either of these can lead to cases where, unlike in the prior work, the addition of fake agents leads to a gain in welfare. This implies that in such cases, if fake agents are absent or are not adequately present, an altruistic platform could artificially introduce fake actions to effect improved learning. 

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2. Contamination Bias in Observational Research on Child Maltreatment: Prevalence, Impact, and Methodological Solutions for Improving the Accuracy of Causal Effects across Public Health Outcomes

   Shenk, CE (September 2025, International Society for Traumatic Stress Studies)

   Contamination occurs in observational research on child maltreatment when individuals assigned to a comparison condition have, unbeknownst to investigators, been exposed to maltreatment. Contamination is a major threat because it biases the statistical significance and magnitude of maltreatment effects, leading to replication failures and an underestimation of the public health impacts of child maltreatment. Despite its presence, there are no established solutions for addressing contamination in child maltreatment research. This symposium brings together leading experts who will present cutting-edge research addressing a range of critical topics for detecting and correcting contamination in observational research on child maltreatment: 1) a conceptual foundation for what contamination is and how it occurs, 2) the prevalence of contamination across independent and international research, 3) the impact contamination has on the direction, statistical significance, and magnitude of maltreatment effects for a range of public health outcomes in nationally-representative and multi-wave designs, and 4) innovative methodological solutions for detecting and correcting contamination. This symposium will provide needed information for child maltreatment researchers to correct contamination in observational research and improve the accuracy, and therefore replicability, of casual effects across public health outcomes. A formal discussion and integration of results presented in this symposium will also aid trauma researchers at large, as contamination can occur in any observational research design. 

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3. Galaxy bias in the era of LSST: perturbative bias expansions

   https://doi.org/10.1088/1475-7516/2024/02/015  

   Nicola, Andrina; Hadzhiyska, Boryana; Findlay, Nathan; García-García, Carlos; Alonso, David; Slosar, Anže; Guo, Zhiyuan; Kokron, Nickolas; Angulo, Raúl; Aviles, Alejandro; et al (February 2024, Journal of Cosmology and Astroparticle Physics)

   Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale ofk_max = 0.4 Mpc^-1for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data. 

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4. Overlap in observational studies with high-dimensional covariates

   https://doi.org/10.1016/j.jeconom.2019.10.014  

   D’Amour, Alexander; Ding, Peng; Feller, Avi; Lei, Lihua; Sekhon, Jasjeet (April 2021, Journal of Econometrics)

   null (Ed.)
5. Overlap in observational studies with high-dimensional covariates

   D’Amour, A; Ding, P; Feller, A; Lei, L; Sekhon, J (January 2020, Journal of econometrics)

   Estimating causal effects under exogeneity hinges on two key assumptions: unconfoundedness and overlap. Researchers often argue that unconfoundedness is more plausible when more covariates are included in the analysis. Less discussed is the fact that covariate overlap is more difficult to satisfy in this setting. In this paper, we explore the implications of overlap in observational studies with high-dimensional covariates and formalize curse-of-dimensionality argument, suggesting that these assumptions are stronger than investigators likely realize. Our key innovation is to explore how strict overlap restricts global discrepancies between the covariate distributions in the treated and control populations. Exploiting results from information theory, we derive explicit bounds on the average imbalance in covariate means under strict overlap and show that these bounds become more restrictive as the dimension grows large. We discuss how these implications interact with assumptions and procedures commonly deployed in observational causal inference, including sparsity and trimming. 

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