251 citations · 449 across the 9 of their papers we have counts for
13 papers
Concave Processes for Multi-Task Career Trajectories: Evaluating Aging Across Multiple Measures of NBA Performance
Abhijit Brahme, Alexander Franks
NBA athlete performance tends to increase through early career as athletes develop and acclimate to the league, followed by decline due to age-related deterioration in athleticism.…
Sensitivity to Unobserved Confounding in Studies with Factor-structured Outcomes
Jiajing Zheng, Jiaxi Wu, Alexander D'Amour +1
In this work, we propose an approach for assessing sensitivity to unobserved confounding in studies with multiple outcomes. We demonstrate how prior knowledge unique to the multi-o…
Initial recommendations for performing, benchmarking, and reporting single-cell proteomics experiments
Laurent Gatto, Ruedi Aebersold, Juergen Cox +18
Analyzing proteins from single cells by tandem mass spectrometry (MS) has become technically feasible. While such analysis has the potential to accurately quantify thousands of pro…
Bayesian Inference and Partial Identification in Multi-Treatment Causal Inference with Unobserved Confounding
Jiajing Zheng, Alexander D'Amour, Alexander Franks
In causal estimation problems, the parameter of interest is often only partially identified, implying that the parameter cannot be recovered exactly, even with infinite data. Here,…
Learning Gaussian Graphical Models with Latent Confounders
Ke Wang, Alexander Franks, Sang-Yun Oh
Gaussian Graphical models (GGM) are widely used to estimate the network structures in many applications ranging from biology to finance. In practice, data is often corrupted by lat…
Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap
Alexander D'Amour, Alexander Franks
A key condition for obtaining reliable estimates of the causal effect of a treatment is overlap (a.k.a. positivity): the distributions of the features used to perform causal adjust…
Copula-based Sensitivity Analysis for Multi-Treatment Causal Inference with Unobserved Confounding
Jiajing Zheng, Alexander D'Amour, Alexander Franks
Recent work has focused on the potential and pitfalls of causal identification in observational studies with multiple simultaneous treatments. Building on previous work, we show th…
Reducing Subspace Models for Large-Scale Covariance Regression
Alexander Franks
We develop an envelope model for joint mean and covariance regression in the large , small setting. In contrast to existing envelope methods, which improve mean estimates by…
Flexible sensitivity analysis for observational studies without observable implications
Alexander Franks, Alexander D'Amour, Avi Feller
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is theref…
Shared Subspace Models for Multi-Group Covariance Estimation
Alexander Franks, Peter Hoff
We develop a model-based method for evaluating heterogeneity among several p x p covariance matrices in the large p, small n setting. This is done by assuming a spiked covariance m…
Non-standard conditionally specified models for non-ignorable missing data
Alexander M Franks, Edoardo M Airoldi, Donald B Rubin
Data analyses typically rely upon assumptions about missingness mechanisms that lead to observed versus missing data. When the data are missing not at random, direct assumptions ab…
Post-transcriptional regulation across human tissues
Alexander Franks, Edoardo Airoldi, Nikolai Slavov
Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining pr…
Estimating cellular pathways from an ensemble of heterogeneous data sources
Alexander Franks, Florian Markowetz, Edoardo Airoldi
Building better models of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert kn…