Directive Explanations for Counterfactual Values

Author(s): Saurabh Pandey, Alankita Kundu


Machine learning has started playing a significant role in complex decision-making processes. Accelerating this, counterfactual explanations in model explainability gives a brief idea about “what could have been the possible output if the input to the model had been changed in a particular way”. With this, one can understand user behaviour and for any recommendation or user conversion these explanations are very useful to get an estimate of which variable needs to be changed and by how much. Directive explanations for counterfactuals output provide the actions that are required for changing the input variable from state A to state B. These explanations are personalized in accordance with each user that makes this idea very unique. This paper includes a novel approach to get the directive explanations by using Markov Decision Processes and Reinforcement Learning.

The Chartered Data Scientist Designation

Achieve the highest distinction in the data science profession.

Explore more from Association of Data Scientists

Become ADaSci Chapter Lead

As a chapter lead, you will have the opportunity to connect with fellow data professionals in your area, share knowledge and resources, and work together to advance the field of data science.