Abstract
Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a high-dimensional, semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill’s sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important variables predicting enactment.
Highlights
The U.S legislative branch creates laws that impact the lives of hundreds of millions of citizens
Five models are compared across the two time conditions. w2v is the scoring of full bill text with an inversion of word2vec-learned language representations [11]
GLM is a regularized non-negative generalized linear model (GLM) meta-learner over an ensemble of a regularized GLM, a gradient boosted machine and a random forest, which each use only the contextual variables. w2vGLM is the same as GLM except the w2v and w2vTitle predictions are added as two more predictor variables for the three base learners
Summary
The U.S legislative branch creates laws that impact the lives of hundreds of millions of citizens. The Patient Protection and Affordable Care Act (ACA) significantly affected the health care industry and individuals’ health insurance coverage. Bills often consist of hundreds of pages of dense legal language. The ACA is more than 900 pages long. There are thousands of bills under consideration at any given time and only about 4% will become law. Length, and vast quantity of bills, a machine learning approach that leverages bill text is well-suited to forecast bill success and identify the important predictive variables. Despite rapid advancement of machine learning methods, it’s difficult to outperform naive forecasts of rare events because of inherent
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