Session 1 - Causal Inference Reading Group

This first session of the new Causal Inference Reading Group on Monday 13 March at 10am will be led by Dr Mingming Gong and will include a general overview and open discussion of causal.

The session will be held in the Steve Howard Room, Level 5, Melbourne Connect.

Causal Inference is the process of trying to understand the cause-and-effect relationships between different factors in a given system. It involves using statistical and other methods to establish whether changes in one variable cause changes in another variable. For example, it can help researchers determine whether a new drug is actually effective in treating a particular disease, or whether a specific policy intervention is having the desired effect on a social issue. Causal Inference is an important concept in many fields, as it allows us to identify the factors that truly influence outcomes and make informed decisions about how to improve them.

The connection between Causal Inference and AI has become increasingly important in recent years, as more and more organizations seek to use AI to make decisions in a variety of domains. While AI has shown great promise in areas such as image recognition, natural language processing, and predictive analytics, it has also been criticized for its lack of transparency and potential for bias. Causal Inference can help address some of these concerns by providing a framework for understanding how AI models make decisions and the underlying causal relationships between variables. By incorporating causal reasoning into AI models, researchers and practitioners can better understand how different features or inputs contribute to the outcome of interest, and identify potential sources of bias or confounding that could affect the accuracy of the model.

Sessions for the remainder of the year are being planned, please take a moment to tell us about your interests in Causal Inference - https://bit.ly/CausalRG - this will help inform session speakers and content.

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