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文献信息
Journal of Causal Inference publishes papers on theoretical and applied causal research across the range of academic disciplines that use quantitative tools to study causality. The past two decades have seen causal inference emerge as a unified field with a solid theoretical foundation, useful in many of the empirical and behavioral sciences. Journal of Causal Inference aims to provide a common venue for researchers working on causal inference in biostatistics and epidemiology, economics, political science and public policy, cognitive science and formal logic, and any field that aims to understand causality. The journal serves as a forum for this growing community to develop a shared language and study the commonalities and distinct strengths of their various disciplines' methods for causal analysis.
Causal InferenceAverage Treatment EffectPropensity ScoreTreatment EffectCausal EffectUnmeasured ConfoundingSensitivity AnalysisTreatment EffectsRandomized ExperimentsAverage Treatment EffectsCausal ModelsAverage Causal EffectStructural Causal ModelsCovariate BalanceInverse Probability WeightingTargeted Maximum Likelihood EstimationEffect EstimationStatistical CausalityRobust EstimationMediation Analysis
vol.13 (2025)
vol.12 (2024)
vol.11 (2023)
vol.10 (2022)
vol.9 (2021)
vol.8 (2020)
vol.7 (2019)
vol.6 (2018)
vol.5 (2017)
vol.4 (2016)
vol.3 (2015)
vol.2 (2014)
vol.1 (2013)
Jonzon, GustavGabriel, Erin E.Sjolander, ArvidSachs, Michael C.
Hong, TaekwonLu, WenbinYang, ShuGhosh, Pulak
Benard, ClementJosse, Julie