, Palladium [2nd Floor]
If I do X instead of Y, will I get the outcome I want? What about in a new unseen situation? Making predictions alone is pointless, one wants to act in the world. Furthermore one must act in situations that are similar but different to all past experience. The real underlying goal of all decision making is really interventional generalisation: the ability to evaluate hypothetical choices in new unseen situations. Unfortunately data science and statistics has a inordinate focus on observation and statistical significance instead of intervention, counter-factuals and generalisation. Improve your modelling both practically and conceptually with the mental tools presented in this talk.
If I do X instead of Y, will I get the outcome I want (in a novel situation)? Making predictions alone is pointless, one wants to act in the world. Furthermore one must act in situations that are similar but different to all past situations. The real underlying goal of all decision making is interventional generalisation: the ability to evaluate hypothetical choices in new unseen situations.
This talk covers this history and problems of null hypothesis significance testing, the benefits (and limitations) of Bayesian reasoning. Introduces the basics of Pearl-ian causality theory and its treatment of interventions and counter-factuals (things that hypothetically could have happened, but didn't), finally we discuss the next step, interventional generalisation, that is being able to compare the value of hypothetical interventions in new unseen situations. Decisively improve your modelling practically and conceptually with the mental tools in this talk.
Born hacker. Curious human. I've started a couple of companies. I liked AI before it was cool, I swear.