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.

…While generation of model predictions and explaining key features influencing the outcomes is helpful, it does not allow making decisions.

To facilitate decision regarding the right interventions needed to reduce attrition, we need answers to below questions :

• What is the impact on final outcomes if the firm decides to make an intervention and organize regular quarterly training for its staff?
• How can we compare the impact of different competing interventions, say organizing quarterly trainings with that of arranging regular senior leadership connect?