5,720 research outputs found
False Discovery Rate Controlled Heterogeneous Treatment Effect Detection for Online Controlled Experiments
Online controlled experiments (a.k.a. A/B testing) have been used as the
mantra for data-driven decision making on feature changing and product shipping
in many Internet companies. However, it is still a great challenge to
systematically measure how every code or feature change impacts millions of
users with great heterogeneity (e.g. countries, ages, devices). The most
commonly used A/B testing framework in many companies is based on Average
Treatment Effect (ATE), which cannot detect the heterogeneity of treatment
effect on users with different characteristics. In this paper, we propose
statistical methods that can systematically and accurately identify
Heterogeneous Treatment Effect (HTE) of any user cohort of interest (e.g.
mobile device type, country), and determine which factors (e.g. age, gender) of
users contribute to the heterogeneity of the treatment effect in an A/B test.
By applying these methods on both simulation data and real-world
experimentation data, we show how they work robustly with controlled low False
Discover Rate (FDR), and at the same time, provides us with useful insights
about the heterogeneity of identified user groups. We have deployed a toolkit
based on these methods, and have used it to measure the Heterogeneous Treatment
Effect of many A/B tests at Snap
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the
current opioid epidemic in the United States. In this work, we analyze medical
and pharmaceutical claims data to draw insights on characteristics of patients
who are more prone to adverse outcomes after an initial synthetic opioid
prescription. Toward this end, we propose a generative model that allows
discovery from observational data of subgroups that demonstrate an enhanced or
diminished causal effect due to treatment. Our approach models these
sub-populations as a mixture distribution, using sparsity to enhance
interpretability, while jointly learning nonlinear predictors of the potential
outcomes to better adjust for confounding. The approach leads to
human-interpretable insights on discovered subgroups, improving the practical
utility for decision suppor
Fast and scalable inference of multi-sample cancer lineages.
Somatic variants can be used as lineage markers for the phylogenetic reconstruction of cancer evolution. Since somatic phylogenetics is complicated by sample heterogeneity, novel specialized tree-building methods are required for cancer phylogeny reconstruction. We present LICHeE (Lineage Inference for Cancer Heterogeneity and Evolution), a novel method that automates the phylogenetic inference of cancer progression from multiple somatic samples. LICHeE uses variant allele frequencies of somatic single nucleotide variants obtained by deep sequencing to reconstruct multi-sample cell lineage trees and infer the subclonal composition of the samples. LICHeE is open source and available at http://viq854.github.io/lichee
BClass: A Bayesian Approach Based on Mixture Models for Clustering and Classification of Heterogeneous Biological Data
Based on mixture models, we present a Bayesian method (called BClass) to classify biological entities (e.g. genes) when variables of quite heterogeneous nature are analyzed. Various statistical distributions are used to model the continuous/categorical data commonly produced by genetic experiments and large-scale genomic projects. We calculate the posterior probability of each entry to belong to each element (group) in the mixture. In this way, an original set of heterogeneous variables is transformed into a set of purely homogeneous characteristics represented by the probabilities of each entry to belong to the groups. The number of groups in the analysis is controlled dynamically by rendering the groups as 'alive' and 'dormant' depending upon the number of entities classified within them. Using standard Metropolis-Hastings and Gibbs sampling algorithms, we constructed a sampler to approximate posterior moments and grouping probabilities. Since this method does not require the definition of similarity measures, it is especially suitable for data mining and knowledge discovery in biological databases. We applied BClass to classify genes in RegulonDB, a database specialized in information about the transcriptional regulation of gene expression in the bacterium Escherichia coli. The classification obtained is consistent with current knowledge and allowed prediction of missing values for a number of genes. BClass is object-oriented and fully programmed in Lisp-Stat. The output grouping probabilities are analyzed and interpreted using graphical (dynamically linked plots) and query-based approaches. We discuss the advantages of using Lisp-Stat as a programming language as well as the problems we faced when the data volume increased exponentially due to the ever-growing number of genomic projects.
Automatic Bayesian Density Analysis
Making sense of a dataset in an automatic and unsupervised fashion is a
challenging problem in statistics and AI. Classical approaches for {exploratory
data analysis} are usually not flexible enough to deal with the uncertainty
inherent to real-world data: they are often restricted to fixed latent
interaction models and homogeneous likelihoods; they are sensitive to missing,
corrupt and anomalous data; moreover, their expressiveness generally comes at
the price of intractable inference. As a result, supervision from statisticians
is usually needed to find the right model for the data. However, since domain
experts are not necessarily also experts in statistics, we propose Automatic
Bayesian Density Analysis (ABDA) to make exploratory data analysis accessible
at large. Specifically, ABDA allows for automatic and efficient missing value
estimation, statistical data type and likelihood discovery, anomaly detection
and dependency structure mining, on top of providing accurate density
estimation. Extensive empirical evidence shows that ABDA is a suitable tool for
automatic exploratory analysis of mixed continuous and discrete tabular data.Comment: In proceedings of the Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI-19
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