Department of Statistics - Conference on Causal Inference in Longitudinal Studies – September 21

Conference on Causal Inference in Longitudinal Studies – September 21 – 23, 2017

Conference on Causal Inference in Longitudinal Studies
September 21-23, 2017
Columbia University

Conference Registration Link
https://www.eventbrite.com/e/conference-on-causal-inference-in-longitudinal-studies-tickets-37733067621

Organizing Committee: Andrew Gelman, Michael Sobel, Jose Zubizaretta

Date: September 21 – 23
Location: Union Theological Seminary – Social Hall

Conference Program

Thursday, September 21, 2017: tutorial

14:15 15:00 Miguel Hernan (Harvard University)

15:00 15:45 Miguel Hernan

15:45 16:00 Coffee break

16:00 16:45 Miguel Hernan

16:45 17:30 Miguel Hernan

Friday, September 22, 2017: research talks

9:00 9:15 David Madigan (Columbia University) – Opening Remarks

Research Talks

9:15 10:00 Jamie Robins (Harvard University)

10:00 10:20 Coffee break

10:20 11:05 Fabrizia Mealli (University of Florence)

11:05 11:50 Stijn Vansteelandt (Ghent University)

11:50 14:00 Lunch break

14:00 14:45 Daniel Almirall (University of Michigan)

14:45 15:30 Mark vander Laan (UC Berkeley)

15:30 15:50 Coffee break

15:50 16:35 Miguel Hernan (Harvard University)

16:35 17:20 Alexander Luedtke (Fred Hutchinson Cancer Research

Center)

19:00 Dinner for speakers

Saturday, September 23, 2017: research talks

9:00 9:45 Thomas Richardson (University of Washington)

9:45 10:30 Eric Laber (NC State University)

10:30 10:50 Coffee break

10:50 11:35 Mats Stensrud (University of Oslo)

11:35 12:20 Wenbin Lu (NC State University)

Upcoming Events

Recent Faculty Publications

Pathfinder: Parallel quasi-Newton variational inference.

Andrew Gelman (2022).

Before data analysis: Additional recommendations for designing experiments to learn about the world.

Andrew Gelman (2023).

Toward a taxonomy of trust for probabilistic machine learning. Science Advances, 9(7), eabn3999.

Broderick, T., Gelman, A., Meager, R., Smith, A. L., & Zheng, T. (2023).

Taylor's law of fluctuation scaling for semivariances and higher moments of heavy tailed data, (PNAS November 16,2021)

Brown, M., Cohen, J.E., Tang, C-F., & Yam, S.C.P. (2021).

Privacy-preserving parametric inference: a case for robust statistics. Journal of the American Statistical Association, 116(534), 969-983.

Avella-Medina, M. (2021).

Heavy-tailed distributions, correlations, kurtosis, and Taylor’s law of fluctuation scaling. Proceedings of the Royal Society A 476:20200610.

J. E. Cohen, Richard A. Davis, Gennady Samorodnitsky (2020).

A Proxy Variable View of Shared Confounding. In International Conference on Machine Learning (pp. 10697-10707). PMLR.

Wang, Y., & Blei, D. (2021, July).

Count time series: A methodological review. Journal of the American Statistical Association, 1-15.

Davis, R. A., Fokianos, K., Holan, S. H., Joe, H., Livsey, J., Lund, R., ... & Ravishanker, N. (2021).

High-frequency analysis of parabolic stochastic PDEs. The Annals of Statistics, 48(2), 1143-1167.

Chong, C. (2020).

The continuous categorical: a novel simplex-valued exponential family. In International Conference on Machine Learning (pp. 3637-3647). PMLR.

Gordon-Rodriguez, E., Loaiza-Ganem, G., & Cunningham, J. (2020, November).

On the bias and variance of odds ratio, relative risk and false discovery proportion. Communications in Statistics-Theory and Methods, 1-31.

Pang, G., Alemayehu, D., de la Peña, V., & Klass, M. J. (2020).

Bayesian statistics and modelling. Nature Reviews Methods Primers, 1(1), 1-26.

van de Schoot, R., Depaoli, S., King, R., Kramer, B., Märtens, K., Tadesse, M. G., ... & Yau, C. (2021).

A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis. Journal of the American Statistical Association, (just-accepted), 1-39.

Gu, Y., & Xu, G. (2021).

Self-Tuning Bandits over Unknown Covariate-Shifts. In Algorithmic Learning Theory (pp. 1114-1156). PMLR.

Suk, J., & Kpotufe, S. (2021, March).

An adaptable generalization of Hotelling’s $ T^{2} $ test in high dimension. The Annals of Statistics, 48(3), 1815-1847.

Li, H., Aue, A., Paul, D., Peng, J., & Wang, P. (2020).

An Interaction-based Convolutional Neural Network (ICNN) Towards Better Understanding of COVID-19 X-ray Images. arXiv preprint arXiv:2106.06911.

Lo, S. H., & Yin, Y. (2021).

Latent feature extraction for process data via multidimensional scaling. psychometrika, 85(2), 378-397.

Tang, X., Wang, Z., He, Q., Liu, J., & Ying, Z. (2020).

Joint estimation of parameters in Ising model. The Annals of Statistics, 48(2), 785-810.

Ghosal, P., & Mukherjee, S. (2020).

Convergence to the mean-field game limit: a case study. The Annals of Applied Probability, 30(1), 259-286.

Nutz, M., San Martin, J., & Tan, X. (2020).

Neural clustering processes. In International Conference on Machine Learning (pp. 7455-7465). PMLR.

Pakman, A., Wang, Y., Mitelut, C., Lee, J., & Paninski, L. (2020, November).

Credit Risk, Liquidity, and Bubbles. International Review of Finance, 20(3), 737-746.

Jarrow, R., & Protter, P. (2020).

An asymptotic rate for the LASSO loss. In International Conference on Artificial Intelligence and Statistics (pp. 3664-3673). PMLR.

Rush, C. (2020, June).

Multivariate rank-based distribution-free nonparametric testing using measure transportation. Journal of the American Statistical Association, (just-accepted), 1-45.

Deb, N., & Sen, B. (2021).

Asset pricing with general transaction costs: Theory and numerics. Mathematical Finance, 31(2), 595-648.

Gonon, L., Muhle‐Karbe, J., & Shi, X. (2021).

Estimating causal effects in studies of human brain function: New models, methods and estimands. The annals of applied statistics, 14(1), 452.

Sobel, M. E., & Lindquist, M. A. (2020).

A note on the Screaming Toes game. arXiv preprint arXiv:2006.04805.

Tavaré, S. (2020).

Testing for stationarity of functional time series in the frequency domain. The Annals of Statistics, 48(5), 2505-2547.

Aue, A., & Van Delft, A. (2020).

Distinguishing cause from effect using quantiles: Bivariate quantile causal discovery. In International Conference on Machine Learning (pp. 9311-9323). PMLR.

Tagasovska, N., Chavez-Demoulin, V., & Vatter, T. (2020, November).

Robust estimation of superhedging prices. The Annals of Statistics, 49(1), 508-530

Obłój, J., & Wiesel, J. (2021).

Optimal stopping and worker selection in crowdsourcing: An adaptive sequential probability ratio test framework. Statistica Sinica, 31(1), 519-546.

Li, X., Chen, Y., Chen, X., Liu, J., & Ying, Z. (2021)

Revisiting colocalization via optimal transport. Nature Computational Science, 1(3), 177-178.

Wang, S., & Yuan, M. (2021).

Large‐scale, image‐based tree species mapping in a tropical forest using artificial perceptual learning. Methods in Ecology and Evolution, 12(4), 608-618.

Tang, C., Uriarte, M., Jin, H., C Morton, D., & Zheng, T. (2021).

From Decoupling and Self-Normalization to Machine Learning

Victor H. de la Pena

Capacity-Achieving Sparse Superposition Codes via Approximate Message Passing Decoding

Cynthia Rush, Adam Greig, Ramji Venkataramanan

Slice Sampling on Hamiltonian Trajectories

John P. Cunningham, Benjamin Bloem-Reddy

Feature Augmentation via Nonparametrics and Selection (FANS) in High Dimensional Classification

Fan, J., Feng, Y., Jiang, J. and Tong, X.

Topic-adjusted visibility metric for scientific articles. Ann. Appl. Stat. 10 (2016), no. 1, 1--31.

Linda S. L. Tan, Aik Hui Chan, and Tian Zheng

Conference on Causal Inference in Longitudinal Studies September 21-23, 2017 Columbia University

Conference Registration Link https://www.eventbrite.com/e/conference-on-causal-inference-in-longitudinal-studies-tickets-37733067621