Causal inference framework

Episode C1VL1: The conceptual framework for causal inference in health sciences. In this first video of 𝗖𝗮𝘂𝘀𝗮𝗹𝗶𝘁𝘆 𝘄𝗶𝘁𝗵 𝗕𝗼𝗿𝗶𝘀, I present a conceptual framework for causal inference in health sciences. What is a conceptual framework? Conceptual frameworks describe and connect ideas we use to formulate research questions and interpret results. In causal analysis of observational data, intervention, treatment groups, treatment variable, outcomes, outcome summary, treatment effect, effect size are the main ideas that make up the conceptual framework of causal inference. 0:00 Intro 0:34 Health intervention 2:38 Treatment groups and outcome 4:39 Summary measure and effect size 6:25 Causal attribution 9:53 Take-aways Additional Materials for Your Learning Most of the videos you watch on Causality with Boris come with additional materials. These might include a chapter from one of my books, an occasional diagram, or a short article. I believe these extras will be useful for your learning in my course. With your subscription on my telegram channel, you'll be the first to receive all extras as they are released. FOLLOW ME ON TWITTER @soboleffspaces MY Telegram CHANNEL https://t.me/+CYgih_aByH5kNzYx #causality #CausalInference #ConceptualFramework #CausalReasoning #CausalAnalysis #CausalDiscovery #Intervention #TreatmentGroups #StudyVariable #Outcome #IndividualResultsMayVary #OutcomeVariable #Variability #SummaryStatistic #SummaryMeasure #TreatmentEffect #EffectMeasure #EffectSize #CausalAttribution #CAusalAI #BayesianDataAnalysis #sobolevspaces