Merge pull request #65 from CITCOM-project/remove-print-statements-from-estimators

Removed print statements

Author: jmafoster1

causal_testing/testing/estimators.py

@@ -167,7 +167,6 @@ def estimate_ate(self) -> tuple[float, list[float, float], float]:
         :return: The average treatment effect and the 95% Wald confidence intervals.
         """
         model = self._run_linear_regression()
-        print(model.summary())
         # Create an empty individual for the control and treated
         individuals = pd.DataFrame(1, index=['control', 'treated'], columns=model.params.index)
         individuals.loc['control', list(self.treatment)] = self.control_values
@@ -257,7 +256,6 @@ def estimate_cates(self) -> tuple[float, list[float, float]]:
                 x[f"{a}*{b}"] = x[a] * x[b]
 
         model = self._run_linear_regression()
-        print(model.summary())
         y = model.predict(x)
         treatment_outcome = y.iloc[0]
         control_outcome = y.iloc[1]
@@ -338,7 +336,6 @@ def estimate_ate(self) -> float:
         model.fit(outcome_df, treatment_df, X=effect_modifier_df, W=confounders_df)
 
         # Obtain the ATE and 95% confidence intervals
-        print(dir(model))
         ate = model.ate(effect_modifier_df, T0=self.control_values, T1=self.treatment_values)
         ate_interval = model.ate_interval(effect_modifier_df, T0=self.control_values, T1=self.treatment_values)
         ci_low, ci_high = ate_interval[0], ate_interval[1]

examples/covasim_/vaccinating_elderly/README.md

@@ -7,7 +7,7 @@ the elderly should cause vaccines and vaccinated to decrease (more restrictive p
 are vaccinated), and no change to the maximum doses since this should always be 2.
 
 This case study directly executes Covasim under two input configurations that differ only in their vaccine input: one
-is the default Pfizer vaccine, the other is the same vaccine but additionally sub-targeting the elderly using a method 
+is the default Pfizer vaccine, the other is the same vaccine but additionally sub-targeting the elderly using a method
 provided in the Covasim vaccine tutorial. We execute each of these input configurations 30 times and repeat this for
 four test cases: one focusing on each of the four previously mentioned outputs.
 
@@ -17,9 +17,9 @@ Further details are provided in Section 5.3 (Prioritising the elderly for vaccin
 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 3.07 by running `pip install covasim==3.0.7`.
+2. Additionally, in order to run Covasim, 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 causal_test_vaccine.py`.
 
 This will run Covasim as described above and print out the causal test results for the effect of each input on each
-output.
+output.