This journal focuses on statistical methods for causal inference, particularly in observational studies and randomized controlled trials. It addresses challenges in identifying causal effects, handling missing data, and comparing different study designs. The scope includes developing and applying methods for estimating treatment effects, analyzing heterogeneity, and understanding the role of propensity scores.
Based on the Think.Check.Submit framework by DOAJ, COPE & OASPA. All data from verified open sources.
Publication & Citation Trend
Articles published
Times cited
2019
2020
2021
2022
2023
2024
2025
2026
Source: OpenAlex · Note: citations accumulate over time so older years appear higher
SJR Quartile by Discipline
Scimago ranks this journal separately in each subject category — its quartile can differ by discipline.
Applied MathematicsQ3
Computer Science ApplicationsQ3
Modeling and SimulationQ3
Numerical AnalysisQ3
Statistics and ProbabilityQ3
Subject Classification
Scopus Categories
Applied MathematicsNumerical AnalysisComputer Science ApplicationsStatistics and ProbabilityModeling and Simulation
Research Topics (OpenAlex)
Advanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods and Bayesian InferencePhilosophy and History of ScienceBayesian Modeling and Causal InferenceStatistical Methods in Clinical TrialsHealth Systems, Economic Evaluations, Quality of LifeAdvanced Statistical Methods and ModelsStatistics Education and MethodologiesData Analysis with R