# Lalonde Pandas API Example¶

We’ll run through a quick example using the high-level Python API for the DoSampler. The DoSampler is different from most classic causal effect estimators. Instead of estimating statistics under interventions, it aims to provide the generality of Pearlian causal inference. In that context, the joint distribution of the variables under an intervention is the quantity of interest. It’s hard to represent a joint distribution nonparametrically, so instead we provide a sample from that distribution, which we call a “do” sample.

Here, when you specify an outcome, that is the variable you’re sampling under an intervention. We still have to do the usual process of making sure the quantity (the conditional interventional distribution of the outcome) is identifiable. We leverage the familiar components of the rest of the package to do that “under the hood”. You’ll notice some similarity in the kwargs for the DoSampler.

## Getting the Data¶

[1]:

import os, sys
sys.path.append(os.path.abspath("../../../"))

[2]:

from rpy2.robjects import r as R

#%R install.packages("Matching")
%R library(Matching)
%R data(lalonde)
%R -o lalonde
lalonde.to_csv("lalonde.csv",index=False)

R[write to console]: Loading required package: MASS

R[write to console]: ##
##  Matching (Version 4.9-6, Build Date: 2019-04-07)
##  See http://sekhon.berkeley.edu/matching for additional documentation.
##   Jasjeet S. Sekhon. 2011. Multivariate and Propensity Score Matching
##   Software with Automated Balance Optimization: The Matching package for R.''
##   Journal of Statistical Software, 42(7): 1-52.
##


[3]:

# the data already loaded in the previous cell. we include the import

import pandas as pd



## The causal Namespace¶

We’ve created a “namespace” for pandas.DataFrames containing causal inference methods. You can access it here with lalonde.causal, where lalonde is our pandas.DataFrame, and causal contains all our new methods! These methods are magically loaded into your existing (and future) dataframes when you import dowhy.api.

[4]:

import dowhy.api


Now that we have the causal namespace, lets give it a try!

## The do Operation¶

The key feature here is the do method, which produces a new dataframe replacing the treatment variable with values specified, and the outcome with a sample from the interventional distribution of the outcome. If you don’t specify a value for the treatment, it leaves the treatment untouched:

[5]:

do_df = lalonde.causal.do(x='treat',
outcome='re78',
common_causes=['nodegr', 'black', 'hisp', 'age', 'educ', 'married'],
variable_types={'age': 'c', 'educ':'c', 'black': 'd', 'hisp': 'd',
'married': 'd', 'nodegr': 'd','re78': 'c', 'treat': 'b'})

WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.
INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['treat'] on outcome ['re78']
INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['hisp', 'married', 'educ', 'nodegr', 'U', 'age', 'black']
WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.

WARN: Do you want to continue by ignoring any unobserved confounders? (use proceed_when_unidentifiable=True to disable this prompt) [y/n] y

INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]
INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.
INFO:dowhy.do_sampler:Caution: do samplers assume iid data.


Notice you get the usual output and prompts about identifiability. This is all dowhy under the hood!

We now have an interventional sample in do_df. It looks very similar to the original dataframe. Compare them:

[6]:

lalonde.head()

[6]:

age educ black hisp married nodegr re74 re75 re78 u74 u75 treat
0 37 11 1 0 1 1 0.0 0.0 9930.05 1 1 1
1 22 9 0 1 0 1 0.0 0.0 3595.89 1 1 1
2 30 12 1 0 0 0 0.0 0.0 24909.50 1 1 1
3 27 11 1 0 0 1 0.0 0.0 7506.15 1 1 1
4 33 8 1 0 0 1 0.0 0.0 289.79 1 1 1
[7]:

do_df.head()

[7]:

age educ black hisp married nodegr re74 re75 re78 u74 u75 treat propensity_score weight
0 28 10 1 0 0 1 0.00 2836.510 3196.57 1 0 1 0.378881 2.639348
1 18 10 1 0 0 1 0.00 273.553 5514.37 1 0 0 0.636766 1.570436
2 17 11 1 0 0 1 4080.73 3796.030 0.00 0 0 0 0.649913 1.538667
3 35 10 1 0 0 1 0.00 0.000 4666.24 1 1 1 0.389989 2.564177
4 30 11 1 0 1 1 0.00 9311.940 3982.80 1 0 0 0.579658 1.725155

## Treatment Effect Estimation¶

We could get a naive estimate before for a treatment effect by doing

[8]:

(lalonde[lalonde['treat'] == 1].mean() - lalonde[lalonde['treat'] == 0].mean())['re78']

[8]:

$\displaystyle 1794.3430848752569$

We can do the same with our new sample from the interventional distribution to get a causal effect estimate

[9]:

(do_df[do_df['treat'] == 1].mean() - do_df[do_df['treat'] == 0].mean())['re78']

[9]:

$\displaystyle 978.3142432607156$

We could get some rough error bars on the outcome using the normal approximation for a 95% confidence interval, like

[10]:

import numpy as np
1.96*np.sqrt((do_df[do_df['treat'] == 1].var()/len(do_df[do_df['treat'] == 1])) +
(do_df[do_df['treat'] == 0].var()/len(do_df[do_df['treat'] == 0])))['re78']

[10]:

$\displaystyle 1189.852604362387$

but note that these DO NOT contain propensity score estimation error. For that, a bootstrapping procedure might be more appropriate.

This is just one statistic we can compute from the interventional distribution of 're78'. We can get all of the interventional moments as well, including functions of 're78'. We can leverage the full power of pandas, like

[11]:

do_df['re78'].describe()

[11]:

count      445.000000
mean      5361.635829
std       6456.849676
min          0.000000
25%          0.000000
50%       3881.280000
75%       8061.490000
max      60307.900000
Name: re78, dtype: float64

[12]:

lalonde['re78'].describe()

[12]:

count      445.000000
mean      5300.765138
std       6631.493362
min          0.000000
25%          0.000000
50%       3701.810000
75%       8124.720000
max      60307.900000
Name: re78, dtype: float64


and even plot aggregations, like

[13]:

%matplotlib inline

[14]:

import seaborn as sns

sns.barplot(data=lalonde, x='treat', y='re78')

[14]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f0090dbe470>

[15]:

sns.barplot(data=do_df, x='treat', y='re78')

[15]:

<matplotlib.axes._subplots.AxesSubplot at 0x7f008e699e80>


## Specifying Interventions¶

You can find the distribution of the outcome under an intervention to set the value of the treatment.

[16]:

do_df = lalonde.causal.do(x={'treat': 1},
outcome='re78',
common_causes=['nodegr', 'black', 'hisp', 'age', 'educ', 'married'],
variable_types={'age': 'c', 'educ':'c', 'black': 'd', 'hisp': 'd',
'married': 'd', 'nodegr': 'd','re78': 'c', 'treat': 'b'})

WARNING:dowhy.causal_model:Causal Graph not provided. DoWhy will construct a graph based on data inputs.
INFO:dowhy.causal_graph:If this is observed data (not from a randomized experiment), there might always be missing confounders. Adding a node named "Unobserved Confounders" to reflect this.
INFO:dowhy.causal_model:Model to find the causal effect of treatment ['treat'] on outcome ['re78']
INFO:dowhy.causal_identifier:Common causes of treatment and outcome:['hisp', 'married', 'educ', 'nodegr', 'U', 'age', 'black']
WARNING:dowhy.causal_identifier:If this is observed data (not from a randomized experiment), there might always be missing confounders. Causal effect cannot be identified perfectly.

WARN: Do you want to continue by ignoring any unobserved confounders? (use proceed_when_unidentifiable=True to disable this prompt) [y/n] y

INFO:dowhy.causal_identifier:Instrumental variables for treatment and outcome:[]
INFO:dowhy.do_sampler:Using WeightingSampler for do sampling.
INFO:dowhy.do_sampler:Caution: do samplers assume iid data.

[17]:

do_df.head()

[17]:

age educ black hisp married nodegr re74 re75 re78 u74 u75 treat propensity_score weight
0 37 9 1 0 0 1 0.00 0.00 1067.51 1 1 1 0.405319 2.467191
1 21 9 1 0 0 1 0.00 0.00 0.00 1 1 1 0.379743 2.633358
2 19 10 1 0 0 1 0.00 0.00 3228.50 1 1 1 0.364787 2.741328
3 46 8 1 0 0 1 3165.66 2594.72 0.00 0 0 1 0.432318 2.313113
4 25 13 1 0 0 0 12362.90 3090.73 0.00 0 0 1 0.526156 1.900578

This new dataframe gives the distribution of 're78' when 'treat' is set to 1.

For much more detail on how the do method works, check the docstring:

[18]:

help(lalonde.causal.do)

Help on method do in module dowhy.api.causal_data_frame:

do(x, method='weighting', num_cores=1, variable_types={}, outcome=None, params=None, dot_graph=None, common_causes=None, estimand_type='nonparametric-ate', proceed_when_unidentifiable=False, stateful=False) method of dowhy.api.causal_data_frame.CausalAccessor instance
The do-operation implemented with sampling. This will return a pandas.DataFrame with the outcome
variable(s) replaced with samples from P(Y|do(X=x)).

If the value of x is left unspecified (e.g. as a string or list), then the original values of x are left in
the DataFrame, and Y is sampled from its respective P(Y|do(x)). If the value of x is specified (passed with a
dict, where variable names are keys, and values are specified) then the new DataFrame will contain the
specified values of x.

For some methods, the variable_types field must be specified. It should be a dict, where the keys are
variable names, and values are 'o' for ordered discrete, 'u' for un-ordered discrete, 'd' for discrete, or 'c'
for continuous.

Inference requires a set of control variables. These can be provided explicitly using common_causes, which
contains a list of variable names to control for. These can be provided implicitly by specifying a causal graph
with dot_graph, from which they will be chosen using the default identification method.

When the set of control variables can't be identified with the provided assumptions, a prompt will raise to the
user asking whether to proceed. To automatically over-ride the prompt, you can set the flag
proceed_when_unidentifiable to True.

Some methods build components during inference which are expensive. To retain those components for later
inference (e.g. successive calls to do with different values of x), you can set the stateful flag to True.
Be cautious about using the do operation statefully. State is set on the namespace, rather than the method, so
can behave unpredictably. To reset the namespace and run statelessly again, you can call the reset method.

:param x: str, list, dict: The causal state on which to intervene, and (optional) its interventional value(s).
:param method: The inference method to use with the sampler. Currently, 'mcmc', 'weighting', and
'kernel_density' are supported. The mcmc sampler requires pymc3>=3.7.
:param num_cores: int: if the inference method only supports sampling a point at a time, this will parallelize
sampling.
:param variable_types: dict: The dictionary containing the variable types. Must contain the union of the causal
state, control variables, and the outcome.
:param outcome: str: The outcome variable.
:param params: dict: extra parameters to set as attributes on the sampler object
:param dot_graph: str: A string specifying the causal graph.
:param common_causes: list: A list of strings containing the variable names to control for.
:param estimand_type: str: 'nonparametric-ate' is the only one currently supported. Others may be added later, to allow for specific, parametric estimands.
:param proceed_when_unidentifiable: bool: A flag to over-ride user prompts to proceed when effects aren't
identifiable with the assumptions provided.
:param stateful: bool: Whether to retain state. By default, the do operation is stateless.
:return: pandas.DataFrame: A DataFrame containing the sampled outcome