Jon Green - Böcker

How to overcome the habits that are stealing joy from your lifeOur upside-down culture celebrates vice and undervalues virtue, leading us to feed the wrong habits while we allow life-giving practices to die. It's no wonder we feel empty, exhausted, and directionless.There's a better, more fulfilling way to live, and it doesn't involve looking inside yourself for the answers--because that's not where you'll find them. With his signature wit and wisdom, Jonathan "JP" Pokluda helps you● unpack the age-old virtues we're told to pursue in Scripture● understand how these God-given goals free us to love and live as we wish we would● redefine what's important to you ● reclaim a life of virtuePracticing virtues is not just something you do--it's something done in you, slowly but surely transforming you into the person you were meant to be all along.

Causal 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).