Carl Gold, PhD<p>My PR to the <a href="https://sigmoid.social/tags/EconML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>EconML</span></a> <a href="https://sigmoid.social/tags/PyWhy" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>PyWhy</span></a> <a href="https://sigmoid.social/tags/opensource" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>opensource</span></a> <a href="https://sigmoid.social/tags/causalai" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>causalai</span></a> project was merged! 🎉 I made a small contribution by allowing a flexible choice of evaluation metric for scoring both the first stage and final stage models in Double Machine Learning (<a href="https://sigmoid.social/tags/DML" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>DML</span></a>). Before, only the mean square error (MSE) was implemented. But as an ML practitioner "in the trenches" I have found that MSE is hard to interpret and compare across models. My new functions allow that 🙂 <a href="https://sigmoid.social/tags/CausalInference" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>CausalInference</span></a> <a href="https://sigmoid.social/tags/machinelearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>machinelearning</span></a> <a href="https://sigmoid.social/tags/datascience" class="mention hashtag" rel="nofollow noopener" target="_blank">#<span>datascience</span></a></p>