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Upcoming Webinar

Causal Inference in Real World Evidence: What is it? Why now? 

Thursday 5th February 2026 / 9AM PDT / 11AM EDT / 4PM GMT  / 5PM CET

Causal inference is increasingly used to generate real-world evidence (RWE), by regulatory bodies and health technology assessment.

This webinar introduces what is required to support causal claims, how causality can be evaluated across different study designs, and why this shift is particularly relevant for RWE today.

Key Learnings:

  • Distinguishing correlation from causation in the context of clinical and real-world data (RWD) 
  • When association may be mistaken for causation, and why this presents risk for regulatory and HTA decision-making 
  • When do we currently use causality? Examples of how it is currently used (i.e., RCTs). Sometimes this is not explicitly stated but implied.  
  • What do we need for causality? Assumptions required for causal inference. How these are met for an RCT, but can be met with other study designs as well.
  • Why now? Advances in causal methods and the growing regulatory/HTA reliance on RWE make explicit causal intent essential, as it fundamentally shapes study design, analysis planning, and interpretation.

Meet the Experts: 

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Ryan Batten
Senior Statistician Ryan has over five years of experience working with real-world data. He has used various study designs and datasets to answer causal questions. These include electronic health records, claims data, registries, and wearables. He is also a part-time PhD student in Clinical Epidemiology at Memorial University. Additionally, he holds a MSc in Clinical Epidemiology, and a BEng in Civil Engineering. 
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Josh Enxing
Senior Programmer- Moderator Joshua has over five years of experience working with real-world data in pharmaceutical research. At Phastar, he supports post-market observational studies for global clients, applying advanced statistical and data science techniques across R, Python, SQL, and SAS. His work spans complex real-world data sources and contributes to real-world evidence generation and peer-reviewed publications. He holds a Master’s degree in Applied Mathematics.

Webinars Coming Soon

  • Causal Inference Through a Regulatory Lens 
    How regulators and HTA bodies are evaluating causal claims derived from RWE, and what this means for development strategy, submission planning, and evidentiary risk. 
  • Advanced Topics in Causal Inference for RWE & Decision-Making 
    Explores advanced causal methods which are increasingly relevant to regulatory and HTA decision-making, including doubly robust approaches, methods for handling time-varying confounding, targeted maximum likelihood estimation, and sensitivity analyses for unmeasured confounding.  
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