Causal AI -- Enabling Data Driven Decisions

Understand how Causal AI frameworks and algorithms support decision making tasks like estimating the impact of interventions, counterfactual reasoning and repurposing previously gained knowledge on other domains. AI and Machine Learning solutions have made rapid strides in the last decade and they are being increasingly relied upon to generate predictions based on historical data. However they fall short of expectations when it comes to augmenting human decisions on tasks where there is a need to understand the actual causes behind an outcome, quantifying the impact of different interventions on final outcomes and making policy decisions, perform what if analysis and reasoning for scenarios which have not occurred etc. Let's consider a practical scenario to understand the decision making challenges faced by business and how current AI solutions help address those: While generation of model predictions and explaining key features influencing the outcomes is helpful, it does not allow taking decisions. What will also be of immense help in this situation is to understand the consequences of different actions in hindsight. The above is an example of a counterfactual problems and is more difficult than estimating interventions as the data to answer is not observed and recorded.