class: center, middle, title-slide # Almost Matching Exactly ### for Interpretable Treatment Effect Estimation
.left[###Marco Morucci] .right[
] --- class: animated, slideInRight .slide-header[ Treatment Effect Estimation] ### **High-stakes** Science, policy, business decisions are supported and justfied with causal estimates more and more every year. --- .slide-header[ Should I get the COVID-19 Vaccine? ] ### Let's use treatment effect estimates to decide: - Pfizer, about 90% effectiveness estimated (NEJM, 2020) - Oxford, about 70% effectiveness estimated (Lancet, 2020) ### ** Conclusion **: Yes, I should get the vaccine, **because** causal estimates support its effectiveness*. .footnote[*Disclaimer: this is a stylized toy example that does not reflect how decisions are or should actually be made] --- .slide-header[ Is this justification enough?] --- .slide-header[ References] - Polack, F. P., Thomas, S. J., Kitchin, N., Absalon, J., Gurtman, A., Lockhart, S., ... & Gruber, W. C. (2020). Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine. New England Journal of Medicine, 383(27), 2603-2615. - Voysey, M., Clemens, S. A. C., Madhi, S. A., Weckx, L. Y., Folegatti, P. M., Aley, P. K., ... & Bijker, E. (2021). Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK. The Lancet, 397(10269), 99-111.