This is a short tutorial for users that want to get into the thick of things right away. However, we highly recommend that users read the other docs provided in order to get a feel for all of the functionality provided in Ananke. The estimators here are based on theory in our paper Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables (Bhattacharya, Nabi, & Shpitser, 2020).

Following are the necessary packages that need to be imported for this tutorial.

from ananke.graphs import ADMG
from ananke.identification import OneLineID
from ananke.estimation import CausalEffect
from ananke.datasets import load_afixable_data
from ananke.estimation import AutomatedIF
import numpy as np

Creating a Causal Graph

In Ananke, the most frequently used causal graph is an acyclic directed mixed graph (ADMG). Roughly, directed edges \(X \rightarrow Y\) indicate \(X\) is a direct cause of \(Y\) and bidirected edges \(X \leftrightarrow Y\) indicate the presence of one or more unmeasured confounders between \(X\) and \(Y.\) Let’s say we are studying the efficacy of a new antiretroviral therapy vs. an old one. Call this the treatment \(T\) where \(T=1\) represents receiving the new drug and \(T=0\) represents receiving the old. Our outcome of interest is the patients CD4 counts post treatment. Thus, our target of interest (in potential outcomes notation) is the counterfactual contrast \(\psi \equiv E[Y(1)] - E[Y(0)].\) This is how one may create a causal graph, with additional variables relevant to the given problem.

vertices = ['Income', 'Insurance', 'ViralLoad', 'Education', 'T', 'Toxicity', 'CD4']
di_edges = [('ViralLoad', 'Income'), ('ViralLoad', 'T'), ('ViralLoad', 'Toxicity'),
            ('Education', 'Income'), ('Education', 'T'), ('Education', 'Toxicity'),
            ('Income', 'Insurance'), ('Insurance', 'T'), ('T', 'Toxicity'), ('Toxicity', 'CD4'), ('T', 'CD4')]
bi_edges = [('Income', 'T'), ('Insurance', 'ViralLoad'), ('Education', 'CD4')]
G = ADMG(vertices, di_edges, bi_edges)

Identification of the Causal Effect

Using Ananke, we can ask whether the effect of a given treatment \(T\) on a given outcome \(Y\) is identified or not. We have implemented the one line ID algorithm provided in Nested Markov Properties for Acyclic Directed Mixed Graphs which is sound and complete in identifying \(p(Y(t))\) or equivalently \(p(Y | do(t))\). We show how to use this in Ananke through the following example.

one_id = OneLineID(graph=G, treatments=['T'], outcomes=['CD4'])

Estimation of the Causal Effect

Ananke provides an easy interface in order to compute causal effects. First, we instantiate a CausalEffect object.

ace_obj = CausalEffect(graph=G, treatment='T', outcome='CD4')  # setting up the CausalEffect object

 Treatment is a-fixable and graph is mb-shielded.

 Available estimators are:

1. IPW (ipw)
2. Outcome regression (gformula)
3. Generalized AIPW (aipw)
4. Efficient Generalized AIPW (eff-aipw)

Suggested estimator is Efficient Generalized AIPW

In this case, it recommends Efficient Generalized AIPW which is to say, that the estimator used to compute the effect in this case looks a lot like Augmented IPW (which is doubly robust). Further, Ananke uses semiparametric theory in order to provide an estimator that achieves the lowest asymptotic variance.

Given the list of estimators, it is up to the user to specify what estimators they want to work with. For instance, if the user decides to use the efficient generalized AIPW, they only need to use the keyword given in front of it, i.e., eff-aipw, when computing the effect. All the nuisance models are fit using generalized linear models provided in the statsmodels. Users interested in using different modeling approaches can refer to the documentation on accessing the functional form of the influence functions at the end of this notebook.

Let’s load up some toy data and use all of the available estimators in order to compute the causal effect.

data = load_afixable_data() # load some pre-simulated data
ace_ipw = ace_obj.compute_effect(data, "ipw")
ace_gformula = ace_obj.compute_effect(data, "gformula")
ace_aipw = ace_obj.compute_effect(data, "aipw")
ace_eff = ace_obj.compute_effect(data, "eff-aipw")
print("ace using IPW = ", ace_ipw)
print("ace using g-formula = ", ace_gformula)
print("ace using AIPW = ", ace_aipw)
print("ace using efficient AIPW = ", ace_eff)
ace using IPW =  0.4917151858666866
ace using g-formula =  0.4968077996596518
ace using AIPW =  0.5005117929752623
ace using efficient AIPW =  0.491715185866723

In addition, the user can run bootstraps and obtain \((1-\alpha)*100\%\) confidence level for the causal effect point estimate. The user needs to specify two arguments: number of bootstraps n_bootstraps and the significance level \(\alpha\) alpha. The confidence interval can then be obtained via bootstrap percentile intervals, as described in All of Nonparametric Statistics. In this case, the call to the compute_effect returns three values: the first corresponds to the effect computed on the original data, the second and third are the pre-specified lower and upper quantiles, i.e., \((\frac{\alpha}{2}, 1 - \frac{\alpha}{2}).\) The default value for \(\alpha\) is set to be \(0.05\). We illustrate this through the following toy example.

np.random.seed(0)  # setting the seed to get consistent numbers in the documentation
ace, Ql, Qu = ace_obj.compute_effect(data, "eff-aipw", n_bootstraps = 5, alpha=0.05)
print(" ace = ", ace, "\n",
      "lower quantile = ", Ql, "\n",
      "upper quantile = ", Qu)
 ace =  0.491715185866723
 lower quantile =  0.45738153193196984
 upper quantile =  0.5898403691123407

While Ananke has its own built-in esimation strategy for every identifiable causal effect concerning a single treatment and outcome, users may be interested in building their own estimators based on the identifying functionals/nonparametric influence functions. This allows users to use their own preferred estimation strategies such as sample splitting, using their preferred machine learning model etc. Below, we provide an example for a new causal graph reflecting some background knowledge and we are interested in the causal effect of the treatment \(T\) on the outcome \(Y.\)

vertices = ['C2', 'C1', 'T', 'M1', 'M2', 'Y']
di_edges = [('C2', 'T'), ('C2', 'M1'), ('C2', 'M2'), ('C2', 'Y'),
            ('C1', 'T'), ('C1', 'M2'),
            ('T', 'M1'), ('M1', 'M2'), ('M2', 'Y'), ('T', 'Y')]
bi_edges = [('T', 'M2'), ('M1', 'Y')]
G = ADMG(vertices, di_edges, bi_edges)
influence = AutomatedIF(G, 'T', 'Y')
print("beta primal = ", influence.beta_primal_, "\n")
print("beta dual = ", influence.beta_dual_, "\n")
print("np-IF = ", influence.nonparametric_if_, "\n")
print("efficient IF = \n", influence.eff_if_, "\n")

 Treatment is p-fixable and graph is mb-shielded.

 Available estimators are:

1. Primal IPW (p-ipw)
2. Dual IPW (d-ipw)
3. APIPW (apipw)
4. Efficient APIPW (eff-apipw)

Suggested estimator is Efficient APIPW

beta primal =  I(T=t) x 1/[p(T|C1,C2)p(M2|M1,C1,T,C2)] x Σ_T p(T|C1,C2)p(M2|M1,C1,T,C2) x Y

beta dual =  [p(M1|T=t,C2)/p(M1|T,C2)] x E[Y|T=t,M1,M2,C2]

np-IF =  E[βprimal or βdual|C2] - E[βprimal or βdual] + E[βprimal or βdual|C1,C2] - E[βprimal or βdual|C2] + E[βdual|C1,T,C2] - E[βdual|C2,C1] + E[βprimal|M1,C1,T,C2] - E[βprimal|C2,C1,T] + E[βdual|M2,C1,C2,M1,T] - E[βdual|C2,C1,T,M1] + E[βprimal|M2,C1,C2,M1,Y,T] - E[βprimal|C2,C1,T,M1,M2]

efficient IF =
 E[βprimal or βdual|C2] - E[βprimal or βdual] + E[βprimal or βdual|C1] - E[βprimal or βdual] + E[βdual|C1,T,C2] - E[βdual|C1,C2] + E[βprimal|M1,T,C2] - E[βprimal|T,C2] + E[βdual|M1,M2,C1,T,C2] - E[βdual|M1,C1,T,C2] + E[βprimal|M1,Y,M2,T,C2] - E[βprimal|T,M1,M2,C2]