Causal Inference Reading Group

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.

We'd love it if you would kindly answer this survey - https://bit.ly/CausalRG - your answers will assist with planning out group sessions.

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In person sessions will start on 13 March 2023 in the Steve Howard Room at Melbourne Connect.

This group is led by Dr Mingming Gong, Senior Lecturer (Data Science), assisted by Erdun Gao.

Causal Inference Reading Group Sessions

Upcoming Discussion Sessions and Readings

DateReadingsPresenter
Nov 8, 2023Coming soonComing soon
Nov 22, 2023Coming soonComing soon
Dec 6, 2023Coming soonComing soon

Past Casual Inference Discussion Sessions and Readings