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README.md

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+# Confounding Adjustment
+
+In scientific experiments, researchers aim to identify cause and effect by
+holding all variables except one constant. Any difference in outcome can then
+be attributed to the manipulated variable.
+
+However, in many practical cases, it is not possible to control the variables
+of interest. For instance, it is unethical to conduct a randomized trial to test
+the effects of smoking on long-term health outcome; yet knowing the answer to this
+question is of extreme importance to policy makers, insurance companies and
+regulatory agencies. Similarly, in social science research, when studying the
+impact of education on income, researchers cannot manipulate individuals' education
+levels while holding all other variables constant.
+
+In these cases, observational data can form the basis for "natural experiments" but
+care must be taken in interpreting these data. One major issue with interpreting these
+data is known as confounding.
+
+A classic example of confounding is the association between coffee consumption and
+heart disease. Initially, a study might find a positive correlation between high
+coffee consumption and increased risk of heart disease. However, this apparent
+relationship could be confounded by the fact that heavy coffee drinkers are more
+likely to also smoke, which is a known risk factor for heart disease. In this case,
+smoking acts as a confounding variable, as it distorts the true relationship between
+coffee consumption and heart disease. To address this, researchers need to adjust for
+smoking status and potentially other relevant variables to accurately assess the
+independent impact of coffee consumption on heart disease risk.
+
+In general, any comparative analysis of two non-randomized population will differ
+systematically in a number of covariate dimensions and these systematic differences
+must be adjusted for as part of any causal inference analysis.
+
 # PyBalance
 
+`pybalance` is a suite of tools in python for performing confounding adjustment
+in non-randomized populations. In `pybalance`, we start with measures of "balance"
+(how similar two populations are) and directly optimize this metric.
+This approach is different, and we think almost always better, from the well-known
+propensity score approach, in which the probability of treatment assignment
+is modelled.
+
 The `pybalance` library implements several routines for optimizing the balance
-between non-random populations. In observational studies, this matching process
-is a key step towards minimizing the potential effects of confounding
-covariates. The official documentation is hosted [here](https://bayer-group.github.io/pybalance/).
-An application of this library to matchng in the pharmaceutical setting is presented here: [here](https://onlinelibrary.wiley.com/doi/10.1002/pst.2352).
+between non-random populations. To learn more about these methods, head on over
+to the [demos](https://bayer-group.github.io/pybalance/02_demos.html). Then give
+the code a spin for yourself by following the
+[installation instructions](https://bayer-group.github.io/pybalance/01_installation.html).
+
+An application of this library to build an external control arm in a pharmaceutical
+setting is presented [here](https://onlinelibrary.wiley.com/doi/10.1002/pst.2352).
 
-## Features
+# Features
 
 - Implements linear and non-linear optimization approaches for matching.
 - Utilizes integer program solvers and evolutionary solvers for optimization.
 - Includes implementation of propensity score matching for comparison.
 - Offers a variety of balance calculators and matchers.
 - Provides visualization tools for analysis.
 - Supports simulation of datasets for testing and demonstration purposes.
+
+# Limitations
+
+At the moment, `pybalance` only implements matching routines. Suport for weighting
+methods is on our roadmap and will appear in a future release.