# Sensitivity Analysis for Regression Models

Sensitivity analysis helps us examine how sensitive a result is against the possibility of unobserved confounding. Two methods are implemented: 1. Cinelli & Hazlett’s robustness value - This method only supports linear regression estimator. - The partial R^2 of treatment with outcome shows how strongly confounders explaining all the residual outcome variation would have to be associated with the treatment to eliminate the estimated effect. - The robustness value measures the minimum strength of association unobserved confounding should have with both treatment and outcome in order to change the conclusions. - Robustness value close to 1 means the treatment effect can handle strong confounders explaining almost all residual variation of the treatment and the outcome. - Robustness value close to 0 means that even very weak confounders can also change the results. - Benchmarking examines the sensitivity of causal inferences to plausible strengths of the omitted confounders. - This method is based on https://carloscinelli.com/files/Cinelli%20and%20Hazlett%20(2020)%20-%20Making%20Sense%20of%20Sensitivity.pdf 2. Ding & VanderWeele’s E-Value - This method supports linear and logistic regression. - The E-value is the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the treatment and the outcome, conditional on the measured covariates, to fully explain away a specific treatment-outcome association. - The minimum E-value is 1, which means that no unmeasured confounding is needed to explain away the observed association (i.e. the confidence interval crosses the null). - Higher E-values mean that stronger unmeasured confounding is needed to explain away the observed association. There is no maximum E-value. - McGowan & Greevy Jr’s benchmarks the E-value against the measured confounders.

## Step 1: Load required packages

[1]:

import os, sys
sys.path.append(os.path.abspath("../../../"))
import dowhy
from dowhy import CausalModel
import pandas as pd
import numpy as np
import dowhy.datasets

# Config dict to set the logging level
import logging.config
DEFAULT_LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'loggers': {
'': {
'level': 'ERROR',
},
}
}

logging.config.dictConfig(DEFAULT_LOGGING)
# Disabling warnings output
import warnings
from sklearn.exceptions import DataConversionWarning
#warnings.filterwarnings(action='ignore', category=DataConversionWarning)


## Step 2: Load the dataset

We create a dataset with linear relationships between common causes and treatment, and common causes and outcome. Beta is the true causal effect.

[2]:

np.random.seed(100)
data = dowhy.datasets.linear_dataset( beta = 10,
num_common_causes = 7,
num_samples = 500,
num_treatments = 1,
stddev_treatment_noise =10,
stddev_outcome_noise = 5
)

[3]:

model = CausalModel(
data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["gml_graph"],
test_significance=None,
)
model.view_model()
from IPython.display import Image, display
display(Image(filename="causal_model.png"))

[3]:

W0 W1 W2 W3 W4 W5 W6 v0 y
0 -0.145062 -0.235286 0.784843 0.869131 -1.567724 -1.290234 0.116096 True 1.386517
1 -0.228109 -0.020264 -0.589792 0.188139 -2.649265 -1.764439 -0.167236 False -16.159402
2 0.868298 -1.097642 -0.109792 0.487635 -1.861375 -0.527930 -0.066542 False -0.702560
3 -0.017115 1.123918 0.346060 1.845425 0.848049 0.778865 0.596496 True 27.714465
4 -0.757347 -1.426205 -0.457063 1.528053 -2.681410 0.394312 -0.687839 False -20.082633

## Step 3: Create Causal Model

Remove one of the common causes to simulate unobserved confounding

[4]:

data["df"] = data["df"].drop("W4", axis = 1)
graph_str = 'graph[directed 1node[ id "y" label "y"]node[ id "W0" label "W0"] node[ id "W1" label "W1"] node[ id "W2" label "W2"] node[ id "W3" label "W3"]  node[ id "W5" label "W5"] node[ id "W6" label "W6"]node[ id "v0" label "v0"]edge[source "v0" target "y"]edge[ source "W0" target "v0"] edge[ source "W1" target "v0"] edge[ source "W2" target "v0"] edge[ source "W3" target "v0"] edge[ source "W5" target "v0"] edge[ source "W6" target "v0"]edge[ source "W0" target "y"] edge[ source "W1" target "y"] edge[ source "W2" target "y"] edge[ source "W3" target "y"] edge[ source "W5" target "y"] edge[ source "W6" target "y"]]'
model = CausalModel(
data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=graph_str,
test_significance=None,
)
model.view_model()
from IPython.display import Image, display
display(Image(filename="causal_model.png"))

[4]:

W0 W1 W2 W3 W5 W6 v0 y
0 -0.145062 -0.235286 0.784843 0.869131 -1.290234 0.116096 True 1.386517
1 -0.228109 -0.020264 -0.589792 0.188139 -1.764439 -0.167236 False -16.159402
2 0.868298 -1.097642 -0.109792 0.487635 -0.527930 -0.066542 False -0.702560
3 -0.017115 1.123918 0.346060 1.845425 0.778865 0.596496 True 27.714465
4 -0.757347 -1.426205 -0.457063 1.528053 0.394312 -0.687839 False -20.082633

## Step 4: Identification

[5]:

identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)
print(identified_estimand)

Estimand type: EstimandType.NONPARAMETRIC_ATE

### Estimand : 1
Estimand name: backdoor
Estimand expression:
d
─────(E[y|W5,W3,W6,W0,W2,W1])
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W0,W2,W1,U) = P(y|v0,W5,W3,W6,W0,W2,W1)

### Estimand : 2
Estimand name: iv
No such variable(s) found!

### Estimand : 3
Estimand name: frontdoor
No such variable(s) found!



## Step 5: Estimation

Currently only Linear Regression estimator is supported for Linear Sensitivity Analysis

[6]:

estimate = model.estimate_effect(identified_estimand,method_name="backdoor.linear_regression")
print(estimate)

*** Causal Estimate ***

## Identified estimand
Estimand type: EstimandType.NONPARAMETRIC_ATE

### Estimand : 1
Estimand name: backdoor
Estimand expression:
d
─────(E[y|W5,W3,W6,W0,W2,W1])
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W0,W2,W1,U) = P(y|v0,W5,W3,W6,W0,W2,W1)

## Realized estimand
b: y~v0+W5+W3+W6+W0+W2+W1
Target units: ate

## Estimate
Mean value: 10.697677486880917



## Step 6a: Refutation and Sensitivity Analysis - Method 1

identified_estimand: An instance of the identifiedEstimand class that provides the information with respect to which causal pathways are employed when the treatment effects the outcome estimate: An instance of CausalEstimate class. The estimate obtained from the estimator for the original data. method_name: Refutation method name simulation_method: “linear-partial-R2” for Linear Sensitivity Analysis benchmark_common_causes: Name of the covariates used to bound the strengths of unobserved confounder percent_change_estimate: It is the percentage of reduction of treatment estimate that could alter the results (default = 1) if percent_change_estimate = 1, the robustness value describes the strength of association of confounders with treatment and outcome in order to reduce the estimate by 100% i.e bring it down to 0. confounder_increases_estimate: confounder_increases_estimate = True implies that confounder increases the absolute value of estimate and vice versa. Default is confounder_increases_estimate = False i.e. the considered confounders pull estimate towards zero effect_fraction_on_treatment: Strength of association between unobserved confounder and treatment compared to benchmark covariate effect_fraction_on_outcome: Strength of association between unobserved confounder and outcome compared to benchmark covariate null_hypothesis_effect: assumed effect under the null hypothesis (default = 0) plot_estimate: Generate contour plot for estimate while performing sensitivity analysis. (default = True). To override the setting, set plot_estimate = False.

[7]:

refute = model.refute_estimate(identified_estimand, estimate ,
simulation_method = "linear-partial-R2",
benchmark_common_causes = ["W3"],
effect_fraction_on_treatment = [ 1,2,3]
)


The x axis shows hypothetical partial R2 values of unobserved confounder(s) with the treatment. The y axis shows hypothetical partial R2 of unobserved confounder(s) with the outcome. The contour levels represent adjusted t-values or estimates for unobserved confounders with hypothetical partialR2 values when these would be included in full regression model. The red line is the critical threshold: confounders with such strength or stronger are sufficient to invalidate the research conclusions.

[8]:

refute.stats

[8]:

{'estimate': 10.697677486880915,
'standard_error': 0.5938735661282946,
'degree of freedom': 492,
't_statistic': 18.01339223872762,
'r2yt_w': 0.39741498372666806,
'partial_f2': 0.6595168698094561,
'robustness_value': 0.5467445572181009,
'robustness_value_alpha': 0.5076289101030926}

[9]:

refute.benchmarking_results

[9]:

0 0.032677 0.230238 9.535964 0.530308 17.981928 8.494016 10.577912
1 0.065354 0.461490 8.331381 0.451241 18.463243 7.444783 9.217979
2 0.098031 0.693855 7.080284 0.346341 20.443123 6.399795 7.760773

### Parameter List for plot function

plot_type: “estimate” or “t-value” critical_value: special reference value of the estimate or t-value that will be highlighted in the plot x_limit: plot’s maximum x_axis value (default = 0.8) y_limit: plot’s minimum y_axis value (default = 0.8) num_points_per_contour: number of points to calculate and plot each contour line (default = 200) plot_size: tuple denoting the size of the plot (default = (7,7)) contours_color: color of contour line (default = blue) String or array. If array, lines will be plotted with the specific color in ascending order. critical_contour_color: color of threshold line (default = red) label_fontsize: fontsize for labelling contours (default = 9) contour_linewidths: linewidths for contours (default = 0.75) contour_linestyles: linestyles for contours (default = “solid”) See : https://matplotlib.org/3.5.0/gallery/lines_bars_and_markers/linestyles.html contours_label_color: color of contour line label (default = black) critical_label_color: color of threshold line label (default = red) unadjusted_estimate_marker: marker type for unadjusted estimate in the plot (default = ‘D’) See: https://matplotlib.org/stable/api/markers_api.html unadjusted_estimate_color: marker color for unadjusted estimate in the plot (default = “black”) adjusted_estimate_marker: marker type for bias adjusted estimates in the plot (default = ‘^’)adjusted_estimate_color: marker color for bias adjusted estimates in the plot (default = “red”) legend_position:tuple denoting the position of the legend (default = (1.6, 0.6))

[10]:

refute.plot(plot_type = 't-value')


The t statistic is the coefficient divided by its standard error. The higher the t-value, the greater the evidence to reject the null hypothesis. According to the above plot,at 5% significance level, the null hypothesis of zero effect would be rejected given the above confounders.

[11]:

print(refute)

Sensitivity Analysis to Unobserved Confounding using R^2 paramterization

Unadjusted Estimates of Treatment ['v0'] :
Coefficient Estimate : 10.697677486880915
Degree of Freedom : 492
Standard Error : 0.5938735661282946
t-value : 18.01339223872762
F^2 value : 0.6595168698094561

Sensitivity Statistics :
Partial R2 of treatment with outcome : 0.39741498372666806
Robustness Value : 0.5467445572181009

Interpretation of results :
Any confounder explaining less than 54.67% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0

For a significance level of 5.0%, any confounder explaining more than 50.76% percent of the residual variance of both the treatment and the outcome would be strong enough to make the estimated effect not 'statistically significant'

If confounders explained 100% of the residual variance of the outcome, they would need to explain at least 39.74% of the residual variance of the treatment to bring down the estimated effect to 0



## Step 6b: Refutation and Sensitivity Analysis - Method 2

simulated_method_name: “e-value” for E-value

• num_points_per_contour: number of points to calculate and plot for each contour (Default = 200)

• plot_size: size of the plot (Default = (6.4,4.8))

• contour_colors: colors for point estimate and confidence limit contour (Default = [“blue”, “red])

• xy_limit: plot’s maximum x and y value. Default is 2 x E-value. (Default = None)

[12]:

refute = model.refute_estimate(identified_estimand, estimate ,
simulation_method = "e-value",
)

• The x axis shows hypothetical values of the risk ratio of an unmeasured confounder at different levels of the treatment. The y axis shows hypothetical values of the risk ratio of the outcome at different levels of the confounder.

• Points lying on or above the blue contour represent combinations of these risk ratios that would tip (i.e. explain away) the point estimate.

• Points lying on or above the red contour represent combinations of these risk ratios that would tip (i.e. explain away) the confidence interval.

• The green points are Observed Covariate E-values. These measure how much the limiting bound of the confidence interval changes on the E-value scale after a specific covariate is dropped and the estimator is re-fit. The covariate that corresponds to the largest Observed Covariate E-value is labeled.

[13]:

refute.stats

[13]:

{'converted_estimate': 2.457447065735422,
'converted_lower_ci': 2.2288567270043136,
'converted_upper_ci': 2.7094815057979944,
'evalue_estimate': 4.349958364289862,
'evalue_lower_ci': 3.883832733630412,
'evalue_upper_ci': None}

[14]:

refute.benchmarking_results

[14]:

converted_est converted_lower_ci converted_upper_ci observed_covariate_e_value
dropped_covariate
W1 2.992523 2.666604 3.358276 1.681140
W6 2.687656 2.446341 2.952774 1.424835
W3 2.692807 2.422402 2.993396 1.394043
W0 2.599868 2.368550 2.853776 1.320751
W5 2.553701 2.315245 2.816716 1.239411
W2 2.473648 2.217699 2.759138 1.076142
[15]:

print(refute)

Sensitivity Analysis to Unobserved Confounding using the E-value

Estimate (converted to risk ratio scale): 2.457447065735422
Lower 95% CI (converted to risk ratio scale): 2.2288567270043136
Upper 95% CI (converted to risk ratio scale): 2.7094815057979944

Sensitivity Statistics:
E-value for point estimate: 4.349958364289862
E-value for lower 95% CI: 3.883832733630412
E-value for upper 95% CI: None
Largest Observed Covariate E-value: 1.6811401167656803 (W1)

Interpretation of results:
Unmeasured confounder(s) would have to be associated with a 4.35-fold increase in the risk of y, and must be 4.35 times more prevalent in v0, to explain away the observed point estimate.
Unmeasured confounder(s) would have to be associated with a 3.88-fold increase in the risk of y, and must be 3.88 times more prevalent in v0, to explain away the observed confidence interval.


We now run the sensitivity analysis for the same dataset but without dropping any variable. We get a robustness value goes from 0.55 to 0.95 which means that treatment effect can handle strong confounders explaining almost all residual variation of the treatment and the outcome.

[16]:

np.random.seed(100)
data = dowhy.datasets.linear_dataset( beta = 10,
num_common_causes = 7,
num_samples = 500,
num_treatments = 1,
stddev_treatment_noise=10,
stddev_outcome_noise = 1
)

[17]:

model = CausalModel(
data=data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["gml_graph"],
test_significance=None,
)
model.view_model()
from IPython.display import Image, display
display(Image(filename="causal_model.png"))

[17]:

W0 W1 W2 W3 W4 W5 W6 v0 y
0 -0.145062 -0.235286 0.784843 0.869131 -1.567724 -1.290234 0.116096 True 6.311809
1 -0.228109 -0.020264 -0.589792 0.188139 -2.649265 -1.764439 -0.167236 False -12.274406
2 0.868298 -1.097642 -0.109792 0.487635 -1.861375 -0.527930 -0.066542 False -6.487561
3 -0.017115 1.123918 0.346060 1.845425 0.848049 0.778865 0.596496 True 24.653183
4 -0.757347 -1.426205 -0.457063 1.528053 -2.681410 0.394312 -0.687839 False -13.770396
[18]:

identified_estimand = model.identify_effect(proceed_when_unidentifiable=True)
print(identified_estimand)

Estimand type: EstimandType.NONPARAMETRIC_ATE

### Estimand : 1
Estimand name: backdoor
Estimand expression:
d
─────(E[y|W5,W3,W6,W0,W2,W1,W4])
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W0,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W0,W2,W1,W4)

### Estimand : 2
Estimand name: iv
No such variable(s) found!

### Estimand : 3
Estimand name: frontdoor
No such variable(s) found!


[19]:

estimate = model.estimate_effect(identified_estimand,method_name="backdoor.linear_regression")
print(estimate)

*** Causal Estimate ***

## Identified estimand
Estimand type: EstimandType.NONPARAMETRIC_ATE

### Estimand : 1
Estimand name: backdoor
Estimand expression:
d
─────(E[y|W5,W3,W6,W0,W2,W1,W4])
d[v₀]
Estimand assumption 1, Unconfoundedness: If U→{v0} and U→y then P(y|v0,W5,W3,W6,W0,W2,W1,W4,U) = P(y|v0,W5,W3,W6,W0,W2,W1,W4)

## Realized estimand
b: y~v0+W5+W3+W6+W0+W2+W1+W4
Target units: ate

## Estimate
Mean value: 10.081924375588311


[20]:

refute = model.refute_estimate(identified_estimand, estimate ,
simulation_method = "linear-partial-R2",
benchmark_common_causes = ["W3"],
effect_fraction_on_treatment = [ 1,2,3])

/__w/dowhy/dowhy/dowhy/causal_refuters/linear_sensitivity_analyzer.py:199: RuntimeWarning: divide by zero encountered in divide
/__w/dowhy/dowhy/dowhy/causal_refuters/linear_sensitivity_analyzer.py:201: RuntimeWarning: invalid value encountered in divide

[21]:

refute.plot(plot_type = 't-value')

[22]:

print(refute)

Sensitivity Analysis to Unobserved Confounding using R^2 paramterization

Unadjusted Estimates of Treatment ['v0'] :
Coefficient Estimate : 10.081924375588311
Degree of Freedom : 491
Standard Error : 0.10446229543424758
t-value : 96.51256784735547
F^2 value : 18.970826379817503

Sensitivity Statistics :
Partial R2 of treatment with outcome : 0.9499269594066173
Robustness Value : 0.9522057801012398

Interpretation of results :
Any confounder explaining less than 95.22% percent of the residual variance of both the treatment and the outcome would not be strong enough to explain away the observed effect i.e bring down the estimate to 0

For a significance level of 5.0%, any confounder explaining more than 95.04% percent of the residual variance of both the treatment and the outcome would be strong enough to make the estimated effect not 'statistically significant'

If confounders explained 100% of the residual variance of the outcome, they would need to explain at least 94.99% of the residual variance of the treatment to bring down the estimated effect to 0


[23]:

refute.stats

[23]:

{'estimate': 10.081924375588311,
'standard_error': 0.10446229543424758,
'degree of freedom': 491,
't_statistic': 96.51256784735547,
'r2yt_w': 0.9499269594066173,
'partial_f2': 18.970826379817503,
'robustness_value': 0.9522057801012398,
'robustness_value_alpha': 0.950386691319526}

[24]:

refute.benchmarking_results

[24]: