A list of must read articles that I read and thought was great ideia to share them. You will be able to find diverse subjects like machine learning, recommender systems, statistics, business and big data.

Statistics

Inferring causal impact using Bayesian structural time-series models

by Google, 2015

An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must assess to what extent different campaigns have contributed to an incremental lift in web searches, product installs, or sales.

This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place.

A nonparametric sequential test for online randomized experiments

by Abhishek, Vineet and Shie Mannor; 2017.

A propose on a nonparametric sequential test. Use of bootstrap to estimate the likelihood for blocks of data followed by mixture sequential probability ratio test. The proposed test controls type 1 error at any time, has good power, is robust to misspecification in the distribution generating the data, and allows quick inference in online randomized experiments.

Controlled experiments on the web: survey and practical guide

by Ron Kohavi, Roger Longbotham, Dan Sommerfield and Randal M. Henne; 2009

Controlled experiments embody the best scientific design for establishing a causal relationship between changes and their influence on user-observable behavior. Our experience indicates that significant learning and return-on-investment (ROI) are seen when development teams listen to their customers, not to the Highest Paid Person’s Opinion (HiPPO).

Review the important ingredients of running controlled experiments, and discuss their limitations (both technical and organizational) and focus on several areas that are critical to experimentation, including statistical power, sample size, and techniques for variance reduction.

Controlled experiments typically generate large amounts of data, which can be analyzed using data mining techniques to gain deeper understanding of the factors influencing the outcome of interest, leading to new hypotheses and creating a virtuous cycle of improvements.

Data Science

Experimentation - Moving from a Culture of Deployment to a Culture of Learning

by Skyscanner

Through experiments we expose our ideas to empirical evaluation. Naturally, uncertainty follows, but the organisational mind-set one develops around this uncertainty is crucial.

Growth Cycle - Stepping Up Your A/B Tests

by Kevin Shanahan: Peak

Brainstorm, prioritise, define, implement, analyse and learn.

Data Sciente - Key Skills and the Correct Mindset

by Avneesh Saluja, Alok Gupta and Cuky Perez: Airbnb

Data Science is very much an overloaded term that sits at the intersection of Mathematics/Statistics, business domain knowledge, and ‘hacking’. Data Scientists are asked to extract insights from data to drive a company’s metrics.

Creativity has to go into how to set up the problem in the first place, a fast-moving environment, premature optimization (80% of the way there), deliver bottom-line impact and talking the walk in a collaborative environment are some of the key things to look for in Data Scientists.

Machine Learning

Why Should I Trust You? - Explaining the Predictions of Any Classifier

by Marco Tulio Ribeiro: University of Washington

Despite widespread adoption, machine learning models remain mostly black boxes. Understanding the reasons behind predictions is, however, quite important in assessing trust, which is fundamental if one plans to take action based on a prediction. Such understanding also provides insights into the model, which can be used to transform an untrustworthy model or prediction into a trustworthy one.

The History of Deep Learning - Explored Through 6 Code Snippets

by Emil Wallnér

Six snippets of code that made deep learning what it is today. It covers the inventors and the background to their breakthroughs.