The Landscape of Causal Inference: Perspective From Citation Network Analysis
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Publication:5882538
DOI10.1080/00031305.2017.1360794OpenAlexW2742077131WikidataQ58127433 ScholiaQ58127433MaRDI QIDQ5882538
Publication date: 17 March 2023
Published in: The American Statistician (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00031305.2017.1360794
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Cites Work
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- Randomization Inference in a Group–Randomized Trial of Treatments for Depression
- Randomization Inference for Treatment Effect Variation
- Algorithm AS 136: A K-Means Clustering Algorithm
- Causal diagrams for interference
- Coauthorship and citation networks for statisticians
- Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
- Matching methods for causal inference: a review and a look forward
- Semiparametric instrumental variable estimation of treatment response models.
- From the Cover: The structure of scientific collaboration networks
- Estimating peer effects in longitudinal dyadic data using instrumental variables
- War and Wages
- Identification of Causal Effects Using Instrumental Variables
- Doubly robust instrumental variable regression
- Sensitivity Analysis for Instrumental Variables Regression With Overidentifying Restrictions
- The central role of the propensity score in observational studies for causal effects
- Randomization Analysis of Experimental Data: The Fisher Randomization Test
- Identification and Estimation of Local Average Treatment Effects
- The role of the propensity score in estimating dose-response functions
- Matching on the Estimated Propensity Score
- Causal Inference for Statistics, Social, and Biomedical Sciences
- Collective dynamics of ‘small-world’ networks
- Instrumental variables as bias amplifiers with general outcome and confounding
- Bias-Corrected Matching Estimators for Average Treatment Effects
- Large Sample Properties of Matching Estimators for Average Treatment Effects
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Causal Inference With General Treatment Regimes
- Design of observational studies
- Observational studies.