Causal Inference With Applications

This Master’s level course provides a rigorous mathematical survey of causal inference. Causation inferences are critical in science, medicine, policy, and business. This course introduces students to the statistical literature on causal inference, which has emerged in the last 35-40 years and has revolutionised the way statisticians and applied researchers in a variety of disciplines use data to make inferences about causal relationships
We will investigate data collection methods for estimating causal relationships. Students will learn to distinguish between causal and non-causal relationships, which is not always obvious. We will then study and evaluate the various methods students can use to estimate a variety of effects, such as the average treatment effect and the effect of treatment on the treated, such as matching, sub-classification on the propensity score, inverse probability of treatment weighting, and machine learning. At the end, we discuss methods for evaluating some of the assumptions we’ve made, and we look ahead to the extensions we’ll tackle in the course’s sequel.

KEY FEATURE

Causal methods have been adopted and adapted across health disciplines, most notably for single-study analysis.

Researchers frequently use causal methods common in their home disciplines, and how these are chosen, evaluated, implemented, and reported varies greatly.

To the best of our knowledge, no article has yet evaluated trends in the implementation and reporting of causal methods in studies that use individual-level data from multiple studies.

Causality is central to all natural sciences, accounting for the vast majority of what we mean by knowledge. Every physical law in classical physics is a type of deterministic causality.

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Key Topics

    Causal Inference With Applications

    Causal inference combines multiple, disparate datasets with artificial intelligence, machine learning, and subject matter expertise to create “cause and effect” maps known as causal graphs for a variety of scenarios. These graphs assist researchers in determining possible outcomes in novel, previously unexplored situations.
    The emerging field of causal health sciences employs causal inference modelling on massive amounts of observational and experimental health care data that are biassed and heterogeneous. This assists researchers and health care providers in determining the outcomes of various medical interventions, saves money, improves care quality, and speeds up results.

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