Article
Biochemical Research Methods
Qian Gao, Yu Zhang, Jie Liang, Hongwei Sun, Tong Wang
Summary: Propensity score methods are popular for estimating causal effects in non-randomized studies, relying on the unconfoundedness assumption. However, including unnecessary covariates in these models can lead to bias and efficiency loss. The generalized outcome-adaptive LASSO method was proposed in this study for selecting covariates to provide unbiased and statistically efficient estimation, showing high accuracy and precision in simulations.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Biochemical Research Methods
Qian Gao, Yu Zhang, Hongwei Sun, Tong Wang
Summary: This paper reviews the methods for estimating causal effects in observational studies and evaluates their performance in high-dimensional settings. The simulation experiments show that GLiDeR and hdCBPS approaches perform well in terms of estimation accuracy, but further studies are needed for constructing valid confidence intervals.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Biology
P. R. Rosenbaum, D. B. Rubin
Summary: This article emphasizes the importance of design activities prior to examining outcome variables in experimental or observational studies. Balancing the propensity scores is an aspect of the design of observational studies, which can be achieved through matching or balancing on the propensity score. Controlling for observed covariates is a crucial step from association to causation.
Article
Education & Educational Research
Wendy Chan
Summary: As causal inference research progresses, propensity score methods have played an important role in estimation of causal impacts and generalization. Under certain assumptions, propensity score methods can reduce bias and provide valid inferences.
ASIA PACIFIC EDUCATION REVIEW
(2023)
Article
Mathematical & Computational Biology
Siyun Yang, Elizabeth Lorenzi, Georgia Papadogeorgou, Daniel M. Wojdyla, Fan Li, Laine E. Thomas
Summary: This article introduces analytical methods and visualization tools for causal subgroup analysis, including subgroup weighted average treatment effect and overlap weighting method. The proposed methods aim to achieve balance within subgroups and to address the bias-variance tradeoff in SGA. The Connect-S plot is designed for visualizing subgroup covariate balance.
STATISTICS IN MEDICINE
(2021)
Article
Automation & Control Systems
Laura Forastiere, Fabrizia Mealli, Albert Wu, Edoardo M. Airoldi
Summary: In this study, a new covariate-adjustment estimator is proposed to estimate the direct treatment and spillover effects in observational studies on networks. Under assumptions of neighborhood interference and unconfoundedness of individual and neighborhood treatment, the estimator balances individual and neighborhood covariates using a generalized propensity score and conducts adjustment using penalized spline regression. The Bayesian inference strategy accounts for uncertainty in propensity score estimation and incorporates random effects and community detection algorithm to model the correlation among connected units. A simulation study is conducted to evaluate the performance of the proposed estimator on different network topologies.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Economics
Michela Bia, Alessandra Mattei, Andrea Mercatanti
Summary: This study addresses important statistical issues in evaluating the effects of training programs for unemployed individuals by proposing an extended framework to simultaneously tackle truncation and attrition issues. The framework utilizes principal stratification to define causal effects and handle missing data, in addition to applying a Bayesian approach for inference. The findings from evaluating the causal effects of foreign language training programs in Luxembourg may provide insights for improving future employment programs.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Review
Endocrinology & Metabolism
Sean Bankier, Tom Michoel
Summary: Hormonal variations affect phenotypic responses by altering gene expression. Genetic variants related to hormones can be analyzed through gene expression changes, and eQTLs can be used as causal inference tools. Hormone networks driven by transcription factors are associated with eQTLs.
FRONTIERS IN ENDOCRINOLOGY
(2022)
Article
Multidisciplinary Sciences
Manuel Castro, Pedro Ribeiro Mendes Junior, Aurea Soriano-Vargas, Rafael de Oliveira Werneck, Maiara Moreira Goncalves, Leopoldo Lusquino Filho, Renato Moura, Marcelo Zampieri, Oscar Linares, Vitor Ferreira, Alexandre Ferreira, Alessandra Davolio, Denis Schiozer, Anderson Rocha
Summary: In this study, we propose using ensemble models (such as Random Forest) to assess the importance of input features in machine learning models, in order to establish causal relationships between variables. By analyzing oil field production data, we find that our results align with confirmed tracer information, demonstrating the effectiveness of our proposed methodology.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Antonio Anastasio Bruto da Costa, Pallab Dasgupta
Summary: The study aims to extract temporal causal sequences to explain key events in time-series traces, with applications in design debugging, anomaly detection, planning, and root-cause analysis. It utilizes decision trees and interval arithmetic to mine sequences and proposes modified decision tree construction metrics to address the non-determinism introduced by the temporal dimension. The mined sequences are presented in a readable temporal logic language for easy interpretation, illustrated through various examples.
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
(2021)
Article
Mathematics, Interdisciplinary Applications
David A. Stephens, Widemberg S. Nobre, Erica E. M. Moodie, Alexandra M. Schmidt
Summary: This paper studies Bayesian approaches to causal inference using propensity score regression. It emphasizes the importance of considering model mis-specification in causal inference and proposes fully Bayesian inference methods based on decision-theoretic arguments. The authors also propose a computational approach based on the Bayesian bootstrap for inference.
Article
Mathematical & Computational Biology
Youfei Yu, Min Zhang, Xu Shi, Megan E. V. Caram, Roderick J. A. Little, Bhramar Mukherjee
Summary: This article focuses on comparing multiple treatments and binary outcomes using propensity score-based methods, evaluating their relative performance through simulation studies. The methods are applied to assess the effects of four common therapies for castration-resistant advanced-stage prostate cancer. The data consists of medical and pharmacy claims from a large national private health insurance network, with the adverse outcome being admission to the emergency room within a short time window of treatment initiation.
STATISTICS IN MEDICINE
(2021)
Review
Multidisciplinary Sciences
Fan Li, Peng Ding, Fabrizia Mealli
Summary: This paper critically reviews the Bayesian perspective of causal inference based on the potential outcomes framework. It discusses the key elements of Bayesian inference in causal effect estimation, including the assignment mechanism, the general structure of Bayesian inference, and sensitivity analysis. It also highlights unique issues in Bayesian causal inference, such as the role of propensity score, the definition of identifiability, and the choice of priors in different scenarios. Additionally, it emphasizes the importance of covariate overlap and the design stage in Bayesian causal inference, and extends the discussion to complex assignment mechanisms like instrumental variables and time-varying treatments. The strengths and weaknesses of the Bayesian approach to causal inference are identified, and key concepts are illustrated through examples.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2023)
Article
Biology
A. Giffin, B. J. Reich, S. Yang, A. G. Rappold
Summary: This article presents a new causal framework for causal inference in the presence of spatial interference. The framework allows for estimation of direct and spill-over effects, taking into account the influence of exposure at nearby locations. The study demonstrates the efficacy of a generalized propensity score in removing measured confounding.
Editorial Material
Public, Environmental & Occupational Health
Corwin M. Zigler
Summary: The study uses Bayesian g-computation to investigate the causal effect of 6 airborne metal exposures on birth weight linked to power-plant emissions, advocating for framing the analysis of environmental mixtures as an explicit contrast between exposure distributions and focusing on the target trial approach. However, challenges arise in deploying this method in the power plant example when the target trial conflicts with the exposure distribution observed in the data.
AMERICAN JOURNAL OF EPIDEMIOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Andrea Mercatanti, Fan Li, Fabrizia Mealli
STATISTICAL ANALYSIS AND DATA MINING
(2015)
Article
Statistics & Probability
Andrea Mercatanti
AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS
(2013)
Article
Economics
Michela Bia, Alessandra Mattei, Andrea Mercatanti
Summary: This study addresses important statistical issues in evaluating the effects of training programs for unemployed individuals by proposing an extended framework to simultaneously tackle truncation and attrition issues. The framework utilizes principal stratification to define causal effects and handle missing data, in addition to applying a Bayesian approach for inference. The findings from evaluating the causal effects of foreign language training programs in Luxembourg may provide insights for improving future employment programs.
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
(2022)
Article
Statistics & Probability
Fan Li, Andrea Mercatanti, Taneli Makinen, Andrea Silvestrini
Summary: Regression Discontinuity (RD) is a commonly used design for causal inference, but recent applications with ordered categorical variables have posed challenges. This paper proposes an RD approach for such variables and applies it to evaluate the impact of the European Central Bank's corporate sector purchase program (CSPP), finding a significant negative effect on corporate bond spreads at issuance.
ANNALS OF APPLIED STATISTICS
(2021)
Article
Mathematics, Interdisciplinary Applications
Sharmistha Guha, Jerome P. Reiter, Andrea Mercatanti
Summary: In some scenarios, the observational data needed for causal inferences are distributed among two separate data files. Merging these files using traditional two-stage modeling methods is not feasible, as it requires error-prone variables for record linkage. To address this issue, a joint model for simultaneous Bayesian inference on probabilistic linkage and causal effects is proposed. This joint model improves the accuracy of estimated treatment effects and record linkages compared to the traditional two-stage modeling option, as demonstrated through simulation studies and theoretical arguments.
Article
Economics
Francesco Fallucchi, Andrea Mercatanti, Jan Niederreiter
Summary: Using the classifier-Lasso method, we identified three distinct types of players in both fixed group and regrouped group experiments. In fixed groups, the majority of players exhibited reciprocal behavior towards opponents' previous choices, while regrouped experiments showed a lower proportion of reciprocators with no significant association between reciprocators and average efforts.
INTERNATIONAL JOURNAL OF GAME THEORY
(2021)
Article
Business, Finance
Andrea Mercatanti, Taneli Makinen, Andrea Silvestrini
JOURNAL OF INTERNATIONAL MONEY AND FINANCE
(2019)
Article
Statistics & Probability
Andrea Mercatanti, Fan Li
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
(2017)
Article
Computer Science, Interdisciplinary Applications
A Mercatanti
COMPUTATIONAL STATISTICS & DATA ANALYSIS
(2004)
