Cleaned up README

README.md

@@ -26,46 +26,43 @@ in a fair and *just* way. JustCause is a work in progress and new contributors a
 
 # Installation
 
-In order to set up the necessary environment:
+If you just want to use the functionality of JustCause, install it with:
+```
+pip install justcause
+```
+Consider using [conda] to create a virtual environment first.
 
-1. create an environment `justcause` with the help of [conda],
+Developers that want to develop and contribute own algorithms and data sets to the JustCause framework, should:
+
+1. clone the repository and change into the directory
+ ```
+ git clone https://github.com/inovex/justcause.git
+ cd justcause
+ ```
+
+2. create an environment `justcause` with the help of [conda],
  ```
  conda env create -f environment.yaml
  ```
-2. activate the new environment with
+3. activate the new environment with
  ```
  conda activate justcause
  ```
-3. install `justcause` with:
+4. install `justcause` with:
  ```
  python setup.py install # or `develop`
  ```
 
 Optional and needed only once after `git clone`:
 
-4. install several [pre-commit] git hooks with:
+5. install several [pre-commit] git hooks with:
  ```
  pre-commit install
  ```
  and checkout the configuration under `.pre-commit-config.yaml`.
  The `-n, --no-verify` flag of `git commit` can be used to deactivate pre-commit hooks temporarily.
 
 
-# Further Work
-Some steps to continue the work on this project would be:
- - Allow to use Confidence Intervals in the learners (see `causalml`, for example) and in the evaluation
- - Implement a fully parametric DGP, following the dimensions roughly outlined in Chapter 4 of the thesis
- - Rewrite the plot functions in `utils.py` to simply take `DataProvider` as inputs and handle the internals within
- the functions.
- - Implement a run-checker that ensures that all methods fit on the data and/or that no complications arise,
- before expensive computation is started.
- (e.g. requested size is to big for given DataProvider)
- - Obviously, add more methods and reference datasets ;)
- - use Sphinx (checkout `docs` folder) to create command reference and some explanations.
- - add some example notebooks in the `notebooks` folder of experiments and parameter studies
- - Check licences of third-party methods and add and note them accordingly. Within the __init__.py of the subpackage
- add a docstring and state the licences and the original authors.
-
 ## Note
 
 This project has been set up using [PyScaffold] 3.2.2 and the [dsproject extension] 0.4.

docs/index.rst

@@ -1,6 +1,6 @@
-=========
-JustCause
-=========
+============
+Introduction
+============
 
 Evaluating causal inference methods in a scientifically thorough way is a cumbersome and error-prone task.
 To foster good scientific practice **JustCause** provides a framework to easily: