{"product_id":"machine-learning-for-experiments-in-the-social-sciences-paperback-9781009168229","title":"Machine Learning for Experiments in the Social Sciences (Paperback \/ softback) 9781009168229","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eMachine Learning for Experiments in the Social Sciences\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cem\u003eThis Element provides theoretical and practical introductions to machine learning for social scientists in applying it to experimental data.\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eJon Green (Author), Mark H. White, II (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781009168229, Cambridge University Press\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 13 April 2023\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e75 pages\u003cbr\u003e22.9 x 15.2 x 0.4 cm, 0.133 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eCausal inference and machine learning are typically introduced in the social sciences separately as theoretically distinct methodological traditions. However, applications of machine learning in causal inference are increasingly prevalent. This Element provides theoretical and practical introductions to machine learning for social scientists interested in applying such methods to experimental data. We show how machine learning can be useful for conducting robust causal inference and provide a theoretical foundation researchers can use to understand and apply new methods in this rapidly developing field. We then demonstrate two specific methods – the prediction rule ensemble and the causal random forest – for characterizing treatment effect heterogeneity in survey experiments and testing the extent to which such heterogeneity is robust to out-of-sample prediction. We conclude by discussing limitations and tradeoffs of such methods, while directing readers to additional related methods available on the Comprehensive R Archive Network (CRAN).\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e1. Introduction\u003cbr\u003e 2. Causal Inference\u003cbr\u003e 3. Exploratory and Reproducible Research\u003cbr\u003e 4. Machine Learning Basics\u003cbr\u003e 5. Bringing it Together\u003cbr\u003e 6. Prediction Rule Ensembles\u003cbr\u003e 7. Causal Random Forest\u003cbr\u003e 8. Conclusion\u003cbr\u003e References.\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Constitution: government \u0026amp; the state [\u003ca title=\"See our other books on Constitution: government \u0026amp; the state\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Constitution:%20government%20\u0026amp;%20the%20state%20%5BJPHC%5D%22\"\u003eJPHC\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Cambridge University Press","offers":[{"title":"Default Title","offer_id":46265394364696,"sku":"9781009168229","price":16.89,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/products\/9781009168229i_ed328e05-9834-4fee-a585-42e0e31cc75b.jpg?v=1696791312","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/machine-learning-for-experiments-in-the-social-sciences-paperback-9781009168229","provider":"Freshly Printed Books","version":"1.0","type":"link"}