arXiv 11 Mar 2019 · Statistics — Machine Learning · 27 citations (OpenAlex)
arXiv:1903.04209 · PDF · DOI · OpenAlex · Extracted main text
We present a novel framework for estimation and inference with the broad class of universal approximators. Estimation is based on the decomposition of model predictions into Shapley values. Inference relies on analyzing the bias and variance properties of individual Shapley components. We show that Shapley value estimation is asymptotically unbiased, and we introduce Shapley regressions as a tool to uncover the true data generating process from noisy data alone. The well-known case of the linear regression is the special case in our framework if the model is linear in parameters. We present theoretical, numerical, and empirical results for the estimation of heterogeneous treatment effects as our guiding example.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Stefan Wager \ Susan Athey (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 1.000 | 5 | 3 | 100% |
| 2 | Erik Strumbelj \ Igor Kononenko (2010) An Efficient Explanation of Individual Classifications Using Game Theory | 0.928 | 4 | 3 | 100% |
| 3 | Susan Athey \ Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 0.928 | 4 | 3 | 100% |
| 4 | Sören Künzel, Jasjeet Sekhon, Peter Bickel \ Bin Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.843 | 3 | 3 | 100% |
| 5 | Scott Lundberg \ Su-In Lee (2017) A Unified Approach to Interpreting Model Predictions | 0.843 | 3 | 3 | 100% |
| 6 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 7 | Lloyd Shapley (1953) A value for n-person games | 0.737 | 3 | 2 | 100% |
| 8 | David Bholat, Nida Broughton, Janna Ter Meer \ Eryk Walczak (2019) Enhancing central bank communications using simple and relatable information | 0.644 | 2 | 2 | 100% |
| 9 | Victor Chernozhukov, Whitney K. Newey \ Rahul Singh (2022) Automatic Debiased Machine Learning of Causal and Structural Effects | 0.644 | 2 | 2 | 100% |
| 10 | Max H. Farrell, Tengyuan Liang \ Sanjog Misra (2021) Deep Neural Networks for Estimation and Inference | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 59 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Inflation Attitudes of Large Language Models | 0.405 | 1 | 1 |