Accelerating Market Access with AI: It's Time to Look Beyond Propensity Score Matching

In the pharmaceutical industry, the need for reliable evidence on the effectiveness of treatments is more critical than ever. While clinical trial results and statistical associations provide valuable insights, they aren’t always possible or don’t answer the most important question: What truly causes the observed outcomes? Whether it’s assessing the impact of a new drug outside of clinical trial populations, comparing outcomes across treatment strategies, or determining the long-term impact of a therapy, causal inference (CI) offers a precise framework for understanding cause-and-effect relationships and making informed decisions.

This webinar will explore how CI helps pharmaceutical researchers move beyond correlation to uncover the underlying mechanisms of drug efficacy, safety, and market performance. We’ll address how CI methods can be applied to both observational data and clinical trials, answering questions like: What happens if we change a treatment protocol? and What would the patient outcomes look like under different conditions?

We will also discuss the regulatory stance on CI in pharmaceutical research and its growing importance in drug development and approval. With a focus on real-world applications, you’ll learn how CI can help answer key business questions such as the comparative effectiveness of drugs, the impact of new treatments on real-world patient outcomes, and the economic benefits of alternative therapies.

In this webinar, Axtria will share perspectives covering:

• The progression from correlation to causation: Association, Intervention, and Counterfactuals, with a focus on pharmaceutical applications
• Why CI is essential for evaluating drug effectiveness and patient outcomes using observational data
• Practical examples of CI in action within the pharmaceutical industry
• Common pitfalls in CI analysis and how to mitigate them.