The Living Thing / Notebooks :

Survey modelling

Adjusting for the Lizardman constant

Usefulness: 🔧
Novelty: 💡
Uncertainty: 🤪 🤪
Incompleteness: 🚧 🚧 🚧

Hierarchical models to adjust for issues such as non random sampling and the varied great difficulties of eliciting human preferences by asking them

Refs

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Bareinboim, Elias, and Judea Pearl. 2016. “Causal Inference and the Data-Fusion Problem.” Proceedings of the National Academy of Sciences 113 (27): 7345–52. https://doi.org/10.1073/pnas.1510507113.

Bareinboim, Elias, Jin Tian, and Judea Pearl. 2014. “Recovering from Selection Bias in Causal and Statistical Inference.” In AAAI, 2410–6. http://ftp.cs.ucla.edu/pub/stat_ser/r425.pdf.

Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. 2012. “A 61-Million-Person Experiment in Social Influence and Political Mobilization.” Nature 489 (7415): 295–98. https://doi.org/10.1038/nature11421.

Broockman, David E., Joshua Kalla, and Jasjeet S. Sekhon. 2016. “The Design of Field Experiments with Survey Outcomes: A Framework for Selecting More Efficient, Robust, and Ethical Designs.” SSRN Scholarly Paper ID 2742869. Rochester, NY: Social Science Research Network. https://papers.ssrn.com/abstract=2742869.

Gao, Yuxiang, Lauren Kennedy, Daniel Simpson, and Andrew Gelman. 2019. “Improving Multilevel Regression and Poststratification with Structured Priors,” August. http://arxiv.org/abs/1908.06716.

Gelman, Andrew. 2007. “Struggles with Survey Weighting and Regression Modeling.” Statistical Science 22 (2): 153–64. https://doi.org/10.1214/088342306000000691.

Gelman, Andrew, and John B. Carlin. 2000. “Poststratification and Weighting Adjustments.” In In. Wiley. http://www.stat.columbia.edu/~gelman/research/published/handbook5.pdf.

Ghitza, Yair, and Andrew Gelman. 2013. “Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups.” American Journal of Political Science 57 (3): 762–76. https://doi.org/10.1111/ajps.12004.

Kennedy, Edward H., Jacqueline A. Mauro, Michael J. Daniels, Natalie Burns, and Dylan S. Small. 2019. “Handling Missing Data in Instrumental Variable Methods for Causal Inference.” Annual Review of Statistics and Its Application 6 (1): 125–48. https://doi.org/10.1146/annurev-statistics-031017-100353.

Kohler, Ulrich, Frauke Kreuter, and Elizabeth A. Stuart. 2019. “Nonprobability Sampling and Causal Analysis.” Annual Review of Statistics and Its Application 6 (1): 149–72. https://doi.org/10.1146/annurev-statistics-030718-104951.

Lerman, Kristina. 2017. “Computational Social Scientist Beware: Simpson’s Paradox in Behavioral Data,” October. http://arxiv.org/abs/1710.08615.

Little, R. J. A. 1993. “Post-Stratification: A Modeler’s Perspective.” Journal of the American Statistical Association 88 (423): 1001–12. https://doi.org/10.1080/01621459.1993.10476368.

Little, Roderick JA. 1991. “Inference with Survey Weights.” Journal of Official Statistics 7 (4): 405.

Rubin, Donald B, and Richard P Waterman. 2006. “Estimating the Causal Effects of Marketing Interventions Using Propensity Score Methodology.” Statistical Science 21 (2): 206–22. https://doi.org/10.1214/088342306000000259.

Shalizi, Cosma Rohilla, and Edward McFowland III. 2016. “Controlling for Latent Homophily in Social Networks Through Inferring Latent Locations,” July. http://arxiv.org/abs/1607.06565.

Shalizi, Cosma Rohilla, and Andrew C. Thomas. 2011. “Homophily and Contagion Are Generically Confounded in Observational Social Network Studies.” Sociological Methods & Research 40 (2): 211–39. https://doi.org/10.1177/0049124111404820.

Yadav, Pranjul, Lisiane Prunelli, Alexander Hoff, Michael Steinbach, Bonnie Westra, Vipin Kumar, and Gyorgy Simon. 2016. “Causal Inference in Observational Data,” November. http://arxiv.org/abs/1611.04660.