arXiv subjects

Min Jin Ha

Publications and source records attributed to Min Jin Ha.

6 recordsLinked to original sources

arXiv2023

Estimating Causal Effects with Hidden Confounding using Instrumental Variables and Environments

Recent works have proposed regression models which are invariant across data collection environments. These estimators often have a causal interpretation under conditions on the environments and type of invariance imposed. One recent example, the Causal Dantzig (CD), is consistent under hidden confounding and represents an alternative to classical instrumental variable estimators such as Two Stage Least Squares (TSLS). In this work we derive the CD as a generalized method of moments (GMM) estimator. The GMM representation leads to several practical results, including 1) creation of the Generalized Causal Dantzig (GCD) estimator which can be applied to problems with continuous environments where the CD cannot be fit 2) a Hybrid (GCD-TSLS combination) estimator which has properties superior to GCD or TSLS alone 3) straightforward asymptotic results for all methods using GMM theory. We compare the CD, GCD, TSLS, and Hybrid estimators in simulations and an application to a Flow Cytometry data set. The newly proposed GCD and Hybrid estimators have superior performance to existing methods in many settings.

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arXiv2021

Bayesian Robust Learning in Chain Graph Models for Integrative Pharmacogenomics

Integrative analysis of multi-level pharmacogenomic data for modeling dependencies across various biological domains is crucial for developing genomic-testing based treatments. Chain graphs characterize conditional dependence structures of such multi-level data where variables are naturally partitioned into multiple ordered layers, consisting of both directed and undirected edges. Existing literature mostly focus on Gaussian chain graphs, which are ill-suited for non-normal distributions with heavy-tailed marginals, potentially leading to inaccurate inferences. We propose a Bayesian robust chain graph model (RCGM) based on random transformations of marginals using Gaussian scale mixtures to account for node-level non-normality in continuous multivariate data. This flexible modeling strategy facilitates identification of conditional sign dependencies among non-normal nodes while still being able to infer conditional dependencies among normal nodes. In simulations, we demonstrate that RCGM outperforms existing Gaussian chain graph inference methods in data generated from various non-normal mechanisms. We apply our method to genomic, transcriptomic and proteomic data to understand underlying biological processes holistically for drug response and resistance in lung cancer cell lines. Our analysis reveals inter- and intra- platform dependencies of key signaling pathways to monotherapies of icotinib, erlotinib and osimertinib among other drugs, along with shared patterns of molecular mechanisms behind drug actions.

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arXiv2021

Sample Selection Bias in Evaluation of Prediction Performance of Causal Models

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However, prediction performance does depend on the selection of training and test sets. Biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance and compare models on this new set. In this setting, the causal models have similar or worse performance compared to standard association-based estimators such as Lasso. Finally, we compare the performance of causal estimators in simulation studies that reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.

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arXiv2020

A Framework for Mediation Analysis with Multiple Exposures, Multivariate Mediators, and Non-Linear Response Models

Mediation analysis seeks to identify and quantify the paths by which an exposure affects an outcome. Intermediate variables which are effected by the exposure and which effect the outcome are known as mediators. There exists extensive work on mediation analysis in the context of models with a single mediator and continuous and binary outcomes. However these methods are often not suitable for multi-omic data that include highly interconnected variables measuring biological mechanisms and various types of outcome variables such as censored survival responses. In this article, we develop a general framework for causal mediation analysis with multiple exposures, multivariate mediators, and continuous, binary, and survival responses. We estimate mediation effects on several scales including the mean difference, odds ratio, and restricted mean scale as appropriate for various outcome models. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption while accommodating continuous exposures. We evaluate the framework and compare it to other methods in extensive simulation studies by assessing bias, type I error and power at a range of sample sizes, disease prevalences, and number of false mediators. Using Kidney Renal Clear Cell Carcinoma data from The Cancer Genome Atlas, we identify proteins which mediate the effect of metabolic gene expression on survival. Software for implementing this unified framework is made available in an R package (https://github.com/longjp/mediateR).

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arXiv2020

Bayesian Structure Learning in Multi-layered Genomic Networks

Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.

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arXiv2014

PenPC: A Two-step Approach to Estimate the Skeletons of High Dimensional Directed Acyclic Graphs

Estimation of the skeleton of a directed acyclic graph (DAG) is of great importance for understanding the underlying DAG and causaleffects can be assessed from the skeleton when the DAG is notidentifiable. We propose a novel method named PenPC toestimate the skeleton of a high-dimensional DAG by a two-stepapproach. We first estimate the non-zero entries of a concentrationmatrix using penalized regression, and then fix the differencebetween the concentration matrix and the skeleton by evaluating aset of conditional independence hypotheses. For high dimensionalproblems where the number of vertices $p$ is in polynomial orexponential scale of sample size $n$, we study the asymptoticproperty of PenPC on two types of graphs: traditionalrandom graphs where all the vertices have the same expected numberof neighbors, and scale-free graphs where a few vertices may have alarge number of neighbors. As illustrated by extensive simulationsand applications on gene expression data of cancer patients, PenPChas higher sensitivity and specificity than the standard-of-the-artmethod, the PC-stable algorithm.

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