Propensity

A comprehensive guide to implementing PSM with your experimental data, including Python codeAI Generated image “PSM in the style of Wassily Kandinsky” using DALL:E 2 — Property of Frank Hopkins (Author)A/B testing is a widely used experimental design in which two or more interventions are compared on an outcome of interest. The goal of A/B testing is to estimate the causal effect of the interventions on the outcome, while controlling for potential confounding variables. Randomisation is often used to achieve balance…

Magnitude of the flood divide as a function of its physioclimatic controls. a,b, Normalized magnitude (that is, divided by the long-term mean river discharge q¯) of the flood divide in the study dataset as a function of the hydrograph recession exponent (a) and the coefficient of variation of daily flows (b). Shaded areas span the 95% variability range of theoretical predictions and provide an estimate of their uncertainties. Gray markers display the…

The functional relationship between consumption and national income is known as Consumption Function. It was introduced by John Maynard Keynes and represents the willingness of households to purchase goods and services at a given income level during a given period of time. It is represented as C = f(Y); where C = Consumption, Y = National Income and f = Functional Relationship. The consumption function is a psychological concept that shows consumption levels at different income levels in an economy. Besides, it is…

The functional relationship between saving and national income is known as Saving Function. It shows the savings of households during a given period of time at a given income level. In alternate terms, the savings function shows different savings levels at different income levels in an economy. Saving function is also known as Propensity to Save and is represented by S = f(Y); where S = Saving, Y = National Income, and f = Functional Relationship. Let’s understand the concept of Saving Function with the help of the…

Understanding the roots of inverse propensity weighting through a simple example.Photo by Diego PH on UnsplashOne of the well-established methods for causal inference is based on the Inverse Propensity Weighting (IPW). In this post we will use a simple example to build an intuition for IPW. Specifically, we will see how IPW is derived from a simple weighted average in order to account for varying treatment assignment rates in causal evaluation.Let’s consider the simple example where we want to estimate the average effect…

An introduction to the world of causal inference with a hands-on example of using one of its most popular methods to answer a causal questionPhoto by Piret Ilver on UnsplashUnderstanding the effect of an action on an observed outcome is important for many fields. For example, how does quitting smoking influence weight gain. This type of question seems easy to answer: just look at a people who have quit smoking and compare their average weight change to those who haven’t quit smoking (also known as unadjusted estimation).…

How to estimate effects from observational dataThis article is the 2nd post in a series on causal effects. In the previous post, we laid a theoretical foundation for causal effects, but there were some lingering practical concerns. Namely, how can we compute causal effects from observational data? Here, I will discuss a set of techniques that do exactly this using something called a propensity score. The discussion will be followed up with example Python code of using these techniques with real-world data.Key…

Credit: Unsplash/CC0 Public Domain Successful start-up ecosystems are characterized by good transport and telecommunications infrastructure, a high population density, a high proportion of foreign citizens, and numerous qualified employees. IfM Bonn researchers found this and published results in their study, "Start-up activity at the district level and in independent cities: What characterizes successful start-up…

A propensity to hope and joy is real riches; one to fear and sorrow real poverty. — David Hume Abstract Marketers invest a lot of time talking about the significance of getting the correct messages to the perfect individuals at the perfect time. Notifying or Emailing when the user is not interested may cause many users to turn off app notifications or report emails spam which blocks all future communications. Marketing comes at a cost both financial and user experience. If there are 100k users on the platform it is wise…