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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. The Cross-Cut Statistic and its Sensitivity to Bias in Observational Studies with Ordered Doses of Treatment

   https://doi.org/10.1111/biom.12373  

   Rosenbaum, Paul R. ( August 2015 , Biometrics)

   A common practice with ordered doses of treatment and ordered responses, perhaps recorded in a contingency table with ordered rows and columns, is to cut or remove a cross from the table, leaving the outer corners—that is, the high-versus-low dose, high-versus-low response corners—and from these corners to compute a risk or odds ratio. This little remarked but common practice seems to be motivated by the oldest and most familiar method of sensitivity analysis in observational studies, proposed by Cornfield et al. (1959), which says that to explain a population risk ratio purely as bias from an unobserved binary covariate, the prevalence ratio of the covariate must exceed the risk ratio. Quite often, the largest risk ratio, hence the one least sensitive to bias by this standard, is derived from the corners of the ordered table with the central cross removed. Obviously, the corners use only a portion of the data, so a focus on the corners has consequences for the standard error as well as for bias, but sampling variability was not a consideration in this early and familiar form of sensitivity analysis, where point estimates replaced population parameters. Here, this cross-cut analysis is examined with the aid of design sensitivity and the power of a sensitivity analysis.

    

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3. The role of covariate balance in observational studies: Covariate Balance in Observational Studies

   https://doi.org/10.1002/nav.21751  

   Sauppe, Jason J. ; Jacobson, Sheldon H. ( June 2017 , Naval Research Logistics (NRL))
4. Robust empirical calibration of p -values using observational data: Robust Empirical Calibration of p -Values Using Observational Data

   https://doi.org/10.1002/sim.6977  

   Schuemie, Martijn J. ; Hripcsak, George ; Ryan, Patrick B. ; Madigan, David ; Suchard, Marc A. ( September 2016 , Statistics in Medicine)
5. Quantitative assessment of ligand bias from bias plots: The bias coefficient “kappa”

   https://doi.org/10.1016/j.bbagen.2023.130428  

   Karl, Kelly ; Rajagopal, Sudarshan ; Hristova, Kalina ( October 2023 , Biochimica et Biophysica Acta (BBA) - General Subjects)