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jpreszlerjpreszler
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increase draws and decrease precision on summaries
1 parent 76d970a commit b5e6dc6

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+36
-28
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2 files changed

+36
-28
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‎causalpy/pymc_experiments.py

Lines changed: 33 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -90,15 +90,19 @@ def print_coefficients(self) -> None:
9090
... time_variable_name="t",
9191
... group_variable_name="group",
9292
... model=cp.pymc_models.LinearRegression(
93-
... sample_kwargs={"random_seed": seed, "progressbar": False}),
93+
... sample_kwargs={
94+
... "draws": 2000,
95+
... "random_seed": seed,
96+
... "progressbar": False
97+
... }),
9498
... )
95-
>>> result.print_coefficients()
99+
>>> result.print_coefficients() # doctest: +NUMBER
96100
Model coefficients:
97-
Intercept 1.08, 94% HDI [1.03, 1.13]
98-
post_treatment[T.True] 0.98, 94% HDI [0.91, 1.06]
99-
group 0.16, 94% HDI [0.09, 0.23]
100-
group:post_treatment[T.True] 0.51, 94% HDI [0.41, 0.61]
101-
sigma 0.08, 94% HDI [0.07, 0.10]
101+
Intercept 1.0, 94% HDI [1.0, 1.1]
102+
post_treatment[T.True] 0.9, 94% HDI [0.9, 1.0]
103+
group 0.1, 94% HDI [0.0, 0.2]
104+
group:post_treatment[T.True] 0.5, 94% HDI [0.4, 0.6]
105+
sigma 0.0, 94% HDI [0.0, 0.1]
102106
"""
103107
print("Model coefficients:")
104108
coeffs = az.extract(self.idata.posterior, var_names="beta")
@@ -352,22 +356,23 @@ def summary(self) -> None:
352356
... formula="actual ~ 0 + a + b + c + d + e + f + g",
353357
... model=cp.pymc_models.WeightedSumFitter(
354358
... sample_kwargs={
359+
... "draws": 2000,
355360
... "target_accept": 0.95,
356361
... "random_seed": seed,
357362
... "progressbar": False,
358363
... }
359364
... ),
360365
... )
361-
>>> result.summary()
366+
>>> result.summary() # doctest: +NUMBER
362367
==================================Pre-Post Fit==================================
363368
Formula: actual ~ 0 + a + b + c + d + e + f + g
364369
Model coefficients:
365-
a 0.33, 94% HDI [0.30, 0.38]
370+
a 0.34, 94% HDI [0.30, 0.38]
366371
b 0.05, 94% HDI [0.01, 0.09]
367372
c 0.31, 94% HDI [0.26, 0.35]
368373
d 0.06, 94% HDI [0.01, 0.10]
369374
e 0.02, 94% HDI [0.00, 0.06]
370-
f 0.20, 94% HDI [0.12, 0.26]
375+
f 0.19, 94% HDI [0.11, 0.26]
371376
g 0.04, 94% HDI [0.00, 0.08]
372377
sigma 0.26, 94% HDI [0.22, 0.30]
373378
"""
@@ -777,6 +782,7 @@ def summary(self) -> None:
777782
... group_variable_name="group",
778783
... model=cp.pymc_models.LinearRegression(
779784
... sample_kwargs={
785+
... "draws": 2000,
780786
... "target_accept": 0.95,
781787
... "random_seed": seed,
782788
... "progressbar": False,
@@ -788,12 +794,12 @@ def summary(self) -> None:
788794
Formula: y ~ 1 + group*post_treatment
789795
<BLANKLINE>
790796
Results:
791-
Causal impact = 0.51, $CI_{94%}$[0.41, 0.61]
797+
Causal impact = 0.51, $CI_{94%}$[0.41, 0.60]
792798
Model coefficients:
793-
Intercept 1.08, 94% HDI [1.03, 1.13]
794-
post_treatment[T.True] 0.98, 94% HDI [0.92, 1.05]
799+
Intercept 1.08, 94% HDI [1.03, 1.12]
800+
post_treatment[T.True] 0.99, 94% HDI [0.92, 1.05]
795801
group 0.16, 94% HDI [0.09, 0.23]
796-
group:post_treatment[T.True] 0.51, 94% HDI [0.41, 0.61]
802+
group:post_treatment[T.True] 0.51, 94% HDI [0.41, 0.60]
797803
sigma 0.08, 94% HDI [0.07, 0.10]
798804
"""
799805

@@ -1018,6 +1024,7 @@ def summary(self) -> None:
10181024
... formula="y ~ 1 + x + treated + x:treated",
10191025
... model=cp.pymc_models.LinearRegression(
10201026
... sample_kwargs={
1027+
... "draws": 2000,
10211028
... "target_accept": 0.95,
10221029
... "random_seed": seed,
10231030
... "progressbar": False,
@@ -1035,9 +1042,9 @@ def summary(self) -> None:
10351042
Discontinuity at threshold = 0.91
10361043
Model coefficients:
10371044
Intercept 0.09, 94% HDI [-0.00, 0.17]
1038-
treated[T.True] 2.45, 94% HDI [1.66, 3.28]
1045+
treated[T.True] 2.45, 94% HDI [1.64, 3.28]
10391046
x 1.32, 94% HDI [1.14, 1.50]
1040-
x:treated[T.True] -3.08, 94% HDI [-4.17, -2.05]
1047+
x:treated[T.True] -3.09, 94% HDI [-4.16, -2.03]
10411048
sigma 0.36, 94% HDI [0.31, 0.41]
10421049
"""
10431050

@@ -1233,23 +1240,24 @@ def summary(self) -> None:
12331240
... pretreatment_variable_name="pre",
12341241
... model=cp.pymc_models.LinearRegression(
12351242
... sample_kwargs={
1243+
... "draws": 2000,
12361244
... "target_accept": 0.95,
12371245
... "random_seed": seed,
12381246
... "progressbar": False,
12391247
... }
12401248
... )
12411249
... )
1242-
>>> result.summary()
1250+
>>> result.summary() # doctest: +NUMBER
12431251
==================Pretest/posttest Nonequivalent Group Design===================
12441252
Formula: post ~ 1 + C(group) + pre
12451253
<BLANKLINE>
12461254
Results:
1247-
Causal impact = 1.88, $CI_{94%}$[1.69, 2.07]
1255+
Causal impact = 1.8, $CI_{94%}$[1.6, 2.0]
12481256
Model coefficients:
1249-
Intercept -0.47, 94% HDI [-1.16, 0.24]
1250-
C(group)[T.1] 1.88, 94% HDI [1.69, 2.07]
1251-
pre 1.05, 94% HDI [0.98, 1.12]
1252-
sigma 0.51, 94% HDI [0.46, 0.56]
1257+
Intercept -0.4, 94% HDI [-1.2, 0.2]
1258+
C(group)[T.1] 1.8, 94% HDI [1.6, 2.0]
1259+
pre 1.0, 94% HDI [0.9, 1.1]
1260+
sigma 0.5, 94% HDI [0.4, 0.5]
12531261
12541262
"""
12551263

@@ -1317,9 +1325,9 @@ class InstrumentalVariable(ExperimentalDesign):
13171325
>>> y = 2 + 3 * X + 3 * e1
13181326
>>> test_data = pd.DataFrame({"y": y, "X": X, "Z": Z})
13191327
>>> sample_kwargs = {
1320-
... "tune": 10,
1321-
... "draws": 20,
1322-
... "chains": 4,
1328+
... "tune": 1,
1329+
... "draws": 5,
1330+
... "chains": 1,
13231331
... "cores": 4,
13241332
... "target_accept": 0.95,
13251333
... "progressbar": False,

‎causalpy/skl_experiments.py

Lines changed: 3 additions & 3 deletions
Original file line numberDiff line numberDiff line change
@@ -657,7 +657,7 @@ def summary(self):
657657
... model=LinearRegression(),
658658
... treatment_threshold=0.5,
659659
... )
660-
>>> result.summary() # doctest: +NORMALIZE_WHITESPACE
660+
>>> result.summary() # doctest: +NORMALIZE_WHITESPACE,+NUMBER
661661
Difference in Differences experiment
662662
Formula: y ~ 1 + x + treated
663663
Running variable: x
@@ -667,8 +667,8 @@ def summary(self):
667667
Discontinuity at threshold = 0.19
668668
Model coefficients:
669669
Intercept 0.0
670-
treated[T.True] 0.19034196317793994
671-
x 1.229600855360073
670+
treated[T.True] 0.19
671+
x 1.23
672672
"""
673673
print("Difference in Differences experiment")
674674
print(f"Formula: {self.formula}")

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