Updated docs

Author: jmafoster1

docs/source/description.rst

@@ -34,18 +34,4 @@ The Causal Testing Framework consists of 3 main components: 1) Causal Specificat
       test should pass or fail based on the results. In the simplest case, this takes the form of an assertion which
       compares the point estimate to the expected causal effect specified in the causal test case.
 
-
-
-#.
-   :doc:`Data Collection <../modules/data_collector>`\ : Data for the system-under-test can be collected in two
-   ways: experimentally or observationally. The former involves executing the system-under-test under controlled
-   conditions which, by design, isolate the causal effect of interest (accurate but expensive), while the latter
-   involves collecting suitable previous execution data and utilising our causal knowledge to draw causal inferences (
-   potentially less accurate but efficient). To collect experimental data, the user must implement a single method which
-   runs the system-under-test with a given input configuration. On the other hand, when dealing with observational data,
-   we automatically check whether the data is suitable for the identified estimand in two steps. First, confirm whether
-   the data contains a column for each variable in the causal DAG. Second, we check
-   for `positivity violations <https://www.youtube.com/watch?v=4xc8VkrF98w>`_. If there are positivity violations, we can
-   provide instructions for an execution that will fill the gap (future work).
-
 For more information on each of these steps, follow the link to their respective documentation.

docs/source/index.rst

@@ -106,7 +106,6 @@ system-under-test that is expected to cause a change to some output(s).
    :maxdepth: 1
    :caption: Module Descriptions
 
-   /modules/data_collector
    /modules/causal_specification
    /modules/causal_tests
 

docs/source/modules/data_collector.rst

@@ -65,19 +65,23 @@ the given output and input and the desired effect. This information is the minim
 
 Before we can run our test case, we first need data. There are two ways to acquire this: 1. run the model with the
 specific input configurations we're interested in, 2. use data from previous model runs. For a small number of specific
-tests where accuracy is critical, the first approach will yield the best results. To do this, you need to instantiate
-the ``ExperimentalDataCollector`` class.
+tests where accuracy is critical, the first approach will yield the best results. To do this, you can use the
+`ExperimentalEstimator` class. This will run the system directly and calculate the causal effect estimate from this.
 
-Where there are many test cases using pre-existing data is likely to be faster. If the program's behaviour can be
+Where there are many test cases, using pre-existing data is likely to be faster. If the program's behaviour can be
 estimated statistically, the results should still be reliable as long as there is enough data for the estimator to work
 as intended. This will vary depending on the program and the estimator. To use this method, simply instantiate
-the ``ObservationalDataCollector`` class with the modelling scenario and a path to the CSV file containing the runtime
-data, e.g.
+one of the other estimator classes with a Pandas dataframe containing the runtime data, e.g.
 
 .. code-block:: python
-
-   obs_df = pd.read_csv('results/data.csv')
-   data_collector = ObservationalDataCollector(modelling_scenario, obs_df)
+   estimator = LinearRegressionEstimator(
+         treatment_variable,
+         treatment_value,
+         control_value,
+         minimal_adjustment_set,
+         outcome_variable,
+         df=pd.read_csv(observational_data_path),
+     )
 
 
 Whether using fresh or pre-existing data, a key aspect of causal inference is estimation. To actually execute a test, we
@@ -99,7 +103,7 @@ various information. Here, we simply assert that the observed result is (on aver
 
 .. code-block:: python
 
-   causal_test_result = causal_test_case.execute_test(estimation_model, data_collector)
+   causal_test_result = causal_test_case.execute_test(estimation_model)
    test_passes = causal_test_case.expected_causal_effect.apply(causal_test_result)
    assert test_passes, "Expected to see a positive change in y."
 

docs/source/usage.rst

@@ -13,15 +13,16 @@ four test cases: one focusing on each of the four previously mentioned outputs.
 
 Further details are provided in Section 5.3 (Prioritising the elderly for vaccination) of the paper.
 
-**Note**: this version of the CTF utilises the observational data collector in order to separate the software execution 
-and testing. Older versions of this framework simulate the data using the custom experimental data collector and the 
-`covasim` package (version 3.0.7) as outlined below. 
+>[!NOTE]
+>This version of the CTF uses observational data to separate the software execution and testing.
+Older versions of this framework simulate the data using a custom experimental data collector and the `covasim`
+package (version 3.0.7) as outlined below.
 
 ## How to run
 To run this case study:
 1. Ensure all project dependencies are installed by running `pip install .` from the top
 level of this directory (instructions are provided in the project README).
-2. Additionally, in order to run Covasim, install version 3.0.7 by running `pip install covasim==3.0.7`.
+2. If necessary, install version 3.0.7 by running `pip install covasim==3.0.7`.
 3. Change directory to `causal_testing/examples/covasim_/vaccinating_elderly`.
 4. Run the command `python example_vaccine.py`.