%0 Journal Article %1 fisher2018visually %A Fisher, Aaron %A Kennedy, Edward H. %D 2018 %K readings stats %T Visually Communicating and Teaching Intuition for Influence Functions %U http://arxiv.org/abs/1810.03260 %X Estimators based on influence functions (IFs) have been shown to be effective in many settings, especially when combined with machine learning techniques. By focusing on estimating a specific target of interest (e.g., the average effect of a treatment), rather than on estimating the full underlying data generating distribution, IF-based estimators are often able to achieve asymptotically optimal mean-squared error. Still, many researchers find IF-based estimators to be opaque or overly technical, which makes their use less prevalent and their benefits less available. To help foster understanding and trust in IF-based estimators, we present tangible, visual illustrations of when and how IF-based estimators can outperform standard ``plug-in'' estimators. The figures we show are based on connections between IFs, gradients, linear approximations, and Newton-Raphson.

@article{fisher2018visually, abstract = {Estimators based on influence functions (IFs) have been shown to be effective in many settings, especially when combined with machine learning techniques. By focusing on estimating a specific target of interest (e.g., the average effect of a treatment), rather than on estimating the full underlying data generating distribution, IF-based estimators are often able to achieve asymptotically optimal mean-squared error. Still, many researchers find IF-based estimators to be opaque or overly technical, which makes their use less prevalent and their benefits less available. To help foster understanding and trust in IF-based estimators, we present tangible, visual illustrations of when and how IF-based estimators can outperform standard ``plug-in'' estimators. The figures we show are based on connections between IFs, gradients, linear approximations, and Newton-Raphson.}, added-at = {2020-01-22T06:42:25.000+0100}, author = {Fisher, Aaron and Kennedy, Edward H.}, biburl = {https://www.bibsonomy.org/bibtex/20ea05a99c933aadb6c8d4e8c23130fb0/kirk86}, description = {[1810.03260] Visually Communicating and Teaching Intuition for Influence Functions}, interhash = {acb97ba2df200abd38c947ca2a2cea42}, intrahash = {0ea05a99c933aadb6c8d4e8c23130fb0}, keywords = {readings stats}, note = {cite arxiv:1810.03260Comment: This manuscript version includes 2 additional supplemental figures to further aid intuition. In total: 4 figures, 36 pages (double spaced)}, timestamp = {2020-01-22T06:42:25.000+0100}, title = {Visually Communicating and Teaching Intuition for Influence Functions}, url = {http://arxiv.org/abs/1810.03260}, year = 2018 }