Causal Inference and Causal Machine Learning with Practical Applications: The paper highlights the concepts of Causal Inference and Causal ML along with different implementation techniques

One of the most important research areas in Machine Learning is to build prescriptive models. This requires understanding and measurement of the causal impact of any proposed treatment, followed by designing optimal strategy based on such causal estimation. Traditional impact measurement frameworks like A-B testing & Randomized Control Trials have certain limitations in terms of feasibility, time constraint, and unknown confounders. Observational Causal Inference techniques can achieve similar and better results in terms of measuring impact of new proposed changes to any systems. Our models serve a wide variety of users who may respond very differently to any changes, so such heterogeneous behavior can be well captured through Causal Machine Learning models which helps in developing better prescriptive recommendations and implementation strategies. The tutorial will cover techniques of observational causal inference like propensity and covariate matching, Causal ML techniques of conditional average treatment effect estimation, using wide variety of algorithms like meta-learners, direct uplift estimation, tree-based algorithms. It will also cover model validation and visualization for causal ML models, implementation of such models in industry case studies.