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causal inference

31 papers and 1 presentation; 31 titles.

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Conference × Year × Title × Authors Extras Pages
PharmaSUG 2026 Embracing Novel Approaches to Automated Causal Inference Framework Laura Watson,
Sherrine Eid		 source 15
PharmaSUG 2026 Operationalizing Real World Data for External Control Arms: An End to End Framework for Rare Disease and Oncology Trials Anbu Damodaran source 7
PHUSE US 2026 A Practical Approach to Causal Inference Sherrine Eid,
Laura Watson		 source
presentation source		 10
PHUSE APAC 2026 Enhancing Interpretation of Propensity Score Matching through Advanced Data Visualization in Real-World Studies KiranKumar Vyas,
Umayal Annamalai		 source
presentation source		 7
PHUSE APAC 2026 Estimation of the Hypothetical Estimands using Longitudinal version of the TMLE Shreya Wahi source
presentation source		 8
PHUSE APAC 2026 External Comparator Studies in the Era of Real-World Data Vishal Goriya,
Anshul Sinha,
Gerd Rippin		 source
presentation source		 5
PHUSE APAC 2026 Leveraging Real-World Evidence (RWE) in Clinical Trial Design: Statistical Challenges and Opportunities Muhammed Farzan K source
presentation source		 6
PHUSE APAC 2026 Targeted Maximum Likelihood Estimation: A Unified Approach for Confounding and Selection Bias Mahjoob O. Majeed,
Noel S. John		 source
presentation source		 6
PharmaSUG 2025 Super Learner for Predictive Modeling and Causal Analysis Honghe Zhao,
Clay Thompson,
Michael Lamm		 source 9
PHUSE EU 2024 Innovation of Causal Inferential Frameworks for Biostatistics in Real-World Data and Evidence Anwesha Roy source
poster source		 8
WUSS 2024 Estimating causal effects of health interventions using instrumental variables Mehrnaz Siavoshi source 7
PharmaSUG 2024 Bayesian Additive Regression Trees for Counterfactual Prediction and Estimation Michael Lamm source 10
SESUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
SGF 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 13
SGF 2020 Causal Effect Estimands: Interpretation, Identification, and Computation Clay Thompson source 20
PharmaSUG 2020 A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education Deanna Naomi Schreiber-Gregory source 14
SESUG 2019 Introduction to Propensity Score Modeling and Treatment Effect Estimation Jason Brinkley source 13
SGF 2019 Analyzing Structural Causal Models using the CALIS Procedure Banoo Madhanagopal,
John Amrhein		 source 23
SGF 2019 Causal Analyses Using Structural Equation Models Banoo Madhanagopal,
John Amrhein		 source 23
SGF 2019 Causal Graph Analysis with the CAUSALGRAPH Procedure Clay Thompson source 21
MWSUG 2018 Propensity Scores and Causal Inference for (and by) a Beginner Bruce S. Lund 18
SGF 2018 Bayesian Networks for Causal Analysis John Amrhein,
Fei Wang		 source 11
SGF 2018 SAS Macros for Computing Causal Mediated Effects in Two- and Three-Wave Longitudinal Models David P. MacKinnon,
Matthew Valente		 source 18
PharmaSUG 2017 Two Roads Diverged in a Narrow Dataset-When Coarsened Exact Matching is More Appropriate than Propensity Score Matching Aran Canes source 7
WUSS 2016 Selection Bias: How Can Propensity Score Utilization Help Control For It? Deanna Naomi Schreiber-Gregory source 13
SGF 2015 Causal Analytics: Testing, Targeting, and Tweaking to Improve Outcomes Jason Pieratt source 8
SGF 2015 Sensing Demand Signals and Shaping Future Demand Using Multi-tiered Causal Analysis Charlie Chase source 8
SGF 2014 Intermittent Demand Forecasting and Multi-tiered Causal Analysis Edward Katz source 11
SGF 2012 Propensity Score Analysis and Assessment of Propensity Score Approaches Using SAS Procedures Rheta E. Lanehart,
Patricia Rodríguez de Gil,
Eun Sook Kim,
Aarti P. Bellara,
Jeffrey D. Kromrey,
Reginald Lee		 source 11
SGF 2008 Integrating Consumer Demand to Improve Shipment Forecasts Charles Chase source 10
SEUGI 2003 Intelligence at the Heart of Retail [presentation] Chris Dunk source 38
SESUG 1994 Critical Steps to Meaningful Causal Data Analysis Deborah Wall 4

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