# Causal Flows

A casual introduction to causal inference for business analytics, by Ken Acquah

### Surrogate Indicies

February 04, 2021

How can we estimate the effects of policies which impact individuals over a long time period.

### Causal Tree Learning For Heterogeneous Treatment Effect Estimation

July 27, 2020

Analysts are often interested how a particular intervention differentially affects individuals within an observed population, given high dimensional data describing each individual's characteristics. In this scenario, what state of the art machine learning technique is best suited for estimating heterogeneous treatment effects?

### Estimating Heterogeneous Treatment Effects

July 02, 2020

Oftentimes, analysts are interested how a particular intervention differentially affects an observed population. Exposure to a particular advertisement, experimental drug, or economic policy might affect different consumers in different ways. How can we estimate causal effects which vary across a population?

### Propensity Score Matching: What Can Go Wrong?

June 27, 2020

What are some of the challenges an analyst must be wary of when using propensity score matching for causal inference tasks?

### Propensity Score Matching

June 17, 2020

How can an analyst 'control' for a large number of confounding variables when they are analyzing causal effects in an observational setting, and have very little control over which individuals receive which treatment?

### Confounding Bias

June 10, 2020

So far, our discussions of causality have been rather straightforward: we've defined models for describing the world and analyzed their implications. In this post I present the obstacles we may face when leveraging these models as well as the 'adjustments' we can make to remove them.

### Estimating Average Treatment Effects

June 07, 2020

When quantifying the causal effect of a proposed intervention, we wish to estimate the average causal effect this intervention will have on individuals in our dataset. How can we estimate average treatment effects and what biases must we be wary of when evaluating our estimation?

### Potential Outcomes Model

June 05, 2020

We’ve defined a language for describing the existing causal relationships between the many interconnected process that make up our universe. Is there a way we describe the extent of these relationships, in order to more wholly characterize causal effects?

### Structural Causal Models

May 27, 2020

How do we represent causal relationships between the many interconnected processes which comprise our universe?

### Getting In To A Causal Flow

May 20, 2020

What is causal inference? Why is it useful? How can you use to amplify your decision-making capabilities?