Add synthimpute demo day post

_posts/2021-12-08-demo-day-22-synthimpute.md

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+---
+title: 'Demo Day: Using synthimpute for data fusion'
+author: "Max Ghenis"
+description: The synthimpute Python package fuses and synthesizes economic datasets with machine learning.
+categories:
+- demo-days
+- python
+- data-fusion
+- synthimpute
+layout: post
+toc: true
+---
+
+{% include youtube.html content='https://youtu.be/e_sk-_yWamY' %}
+---
+
+Suppose a policy analyst sought to estimate the impact of a policy that changed income tax rates and benefit rules while also adding a progressive wealth tax.
+The standard approach is to use a _microsimulation_ model, where the rules are programmed as code, and then to run that program over a representative sample of households.
+Unfortunately, no single US government survey captures all the households characteristics needed to analyze this policy; in particular, the reliable tax and benefit information lies in surveys like the Current Population Survey (CPS), while wealth lies in the Survey of Consumer Finances (SCF).
+
+Assuming the analyst wanted to start with the CPS, they have several options to estimate wealth for households to levy the progressive wealth tax.
+Two typical approaches include:
+1. Linear regression, predicting wealth from other household characteristics common to the CPS and SCF.
+2. Matching, in which each CPS household is matched with the most similar household in the SCF.
+
+Neither of these approaches, however, aim to estimate the _distribution_ of wealth conditional on other characteristics.
+Linear regression explicitly estimates the mean prediction, but that could miss the tails of wealth from whom most of the wealth tax revenue will be collected.
+
+Instead, the analyst could apply _quantile regression_ to estimate the distribution of wealth conditional on other characteristics, and then measure the effectiveness of the estimation using _quantile loss_.
+
+In this Demo Day, I present the concepts of microsimulation, imputation, and quantile loss to motivate the `synthimpute` Python package I've developed with my PolicyEngine colleague Nikhil Woodruff.
+In an experiment predicting wealth on a holdout set from the SCF, my former colleague Deepak Singh and I found that random forests significantly outperform OLS and matching for quantile regression, and this is the approach applied in `synthimpute` for both data fusion and data synthesis.
+The `synthimpute` API will be familiar to users of `scikit-learn` and `statsmodels`, with the difference being that `synthimpute`'s `rf_impute` function returns a random value from the predicted distribution; it can also skew the predictions to meet a target total.
+
+We've used `synthimpute` to fuse data for research reports at the [UBI Center](https://ubicenter.org) and to enhance the [PolicyEngine](https://policyengine.org) web app for UK tax and benefit simulation, and we welcome new users and contributors.
+Feel free to explore the repository or contact me with questions at [[email protected]](mailto:[email protected]).
+
+Resources:
+* [`synthimpute` package on GitHub](https://github.com/PolicyEngine/synthimpute)
+* [Presentation slides](https://docs.google.com/presentation/d/1Hw0zIkbnXZluW8ntjbw_NXryRvjVcg6DDp-btkXXV_o)
+* [UBI Center report on land value taxation in the UK](https://www.ubicenter.org/uk-lvt), using `synthimpute` to impute land value from the UK Wealth and Assets Survey to the Family Resources Survey
+* [PolicyEngine UK carbon tax example](http://policyengine.org/uk/population-impact?carbon_tax=100), using `synthimpute` to impute carbon emissions from the Living Costs and Food Survey to the Family Resources Survey
+* [Notebook comparing random forests to matching and other techniques](https://colab.research.google.com/drive/1E8F7S1Uvfw_3PmpS226Sl1LWV5NBi0CE) using a holdout set from the US Survey of Consumer Finances
+* [My blog post on quantile regression for Towards Data Science](https://towardsdatascience.com/quantile-regression-from-linear-models-to-trees-to-deep-learning-af3738b527c3), which laid the groundwork for `synthimpute`