Feature p value

backend-python/media_impact_monitor/impact.py

@@ -1,4 +1,3 @@
-
 import pandas as pd
 
 from media_impact_monitor.events import get_events
@@ -87,6 +86,7 @@ def get_impact(q: ImpactSearch) -> Impact:
                     mean=impact["mean"].loc[n_days],
                     ci_upper=impact["ci_upper"].loc[n_days],
                     ci_lower=impact["ci_lower"].loc[n_days],
+                    p_value=impact["p_value"].loc[n_days],
                 ),
                 absolute_impact_time_series=[
                     DatedMeanWithUncertainty(**d)

backend-python/media_impact_monitor/impact_estimators/interrupted_time_series.py

@@ -167,5 +167,10 @@ def estimate_mean_impact(
         lambda x: confidence_interval_ttest(x.dropna(), 0.95)[1], axis=1
     )
     return pd.DataFrame(
-        {"mean": average, "ci_lower": ci_lower, "ci_upper": ci_upper}
+        {
+            "mean": average,
+            "ci_lower": ci_lower,
+            "ci_upper": ci_upper,
+            "p_value": 1,  # TODO
+        }
     ), warnings

backend-python/media_impact_monitor/impact_estimators/interrupted_time_series_test.py

@@ -82,7 +82,7 @@ def test_mean_impact_estimates():
     assert impacts_df.index[0] == -4
     assert impacts_df.index[-1] == 6
     assert len(impacts_df) == 4 + 7
-    assert set(impacts_df.columns) == {"mean", "ci_lower", "ci_upper"}
+    assert set(impacts_df.columns) == {"mean", "ci_lower", "ci_upper", "p_value"}
     for i in range(-4, -1):
         mean = impacts_df.loc[i, "mean"]
         assert -50 <= mean <= 50

backend-python/media_impact_monitor/impact_estimators/time_series_regression.py

@@ -1,4 +1,3 @@
-
 import pandas as pd
 import statsmodels.api as sm
 
@@ -69,7 +68,7 @@ def regress(
     return {
         "date": day,
         "mean": model.params[treatment],
-        "p": model.pvalues[treatment],
+        "p_value": model.pvalues[treatment],
         "ci_lower": model.conf_int(alpha=alpha)[0][treatment],
         "ci_upper": model.conf_int(alpha=alpha)[1][treatment],
     }
@@ -103,5 +102,5 @@ def estimate_impact(
             for day in outcome_days
         ]
     )
-    impacts = impacts.set_index("date")[["mean", "ci_lower", "ci_upper"]]
+    impacts = impacts.set_index("date")[["mean", "ci_lower", "ci_upper", "p_value"]]
     return impacts, limitations

backend-python/media_impact_monitor/types_.py

@@ -217,13 +217,15 @@ class MeanWithUncertainty(BaseModel):
     mean: float = Field(description="Mean estimate.")
     ci_upper: float = Field(description="Upper bound of the 95% confidence interval.")
     ci_lower: float = Field(description="Lower bound of the 95% confidence interval.")
+    p_value: float = Field(description="P-value.")
 
 
 class DatedMeanWithUncertainty(BaseModel):
     date: int | date
     mean: float
     ci_upper: float
     ci_lower: float
+    p_value: float
 
 
 class ImpactEstimate(BaseModel):