# DoWhy | Making causal inference easy¶

Amit Sharma (amshar@microsoft.com), Emre Kiciman (emrek@microsoft.com)

As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal reasoning.

Much like machine learning libraries have done for prediction, **“DoWhy” is a Python library that aims to spark causal thinking and analysis**. DoWhy provides a unified interface for causal inference methods and automatically tests many assumptions, thus making inference accessible to non-experts.

For a quick introduction to causal inference, check out amit-sharma/causal-inference-tutorial. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (KDD 2018) conference: causalinference.gitlab.io/kdd-tutorial.

Documentation for DoWhy is available at causalinference.gitlab.io/dowhy.

Contents

- DoWhy | Making causal inference easy
- The need for causal inference
- Sample causal inference analysis in DoWhy
- Installation
- Graphical Models and Potential Outcomes: Best of both worlds
- A unifying language for causal inference
- Model a causal problem
- Identify a target estimand under the model
- Estimate causal effect based on the identified estimand
- Refute the obtained estimate
- Contributing

- Indices and tables

## The need for causal inference¶

Predictive models uncover patterns that connect the inputs and outcome in observed data. To intervene, however, we need to estimate the effect of changing an input from its current value, for which no data exists. Such questions, involving estimating a *counterfactual*, are common in decision-making scenarios.

- Will it work?
- Does a proposed change to a system improve people’s outcomes?

- Why did it work?
- What led to a change in a system’s outcome?

- What should we do?
- What changes to a system are likely to improve outcomes for people?

- What are the overall effects?
- How does the system interact with human behavior?
- What is the effect of a system’s recommendations on people’s activity?

Answering these questions requires causal reasoning. While many methods exist for causal inference, it is hard to compare their assumptions and robustness of results. DoWhy makes three contributions,

- Provides a principled way of modeling a given problem as a causal graph so that all assumptions explicit.
- Provides a unified interface for many popular causal inference methods, combining the two major frameworks of graphical models and potential outcomes.
- Automatically tests for the validity of assumptions if possible and assesses robustness of the estimate to violations.

## Sample causal inference analysis in DoWhy¶

Most DoWhy analyses for causal inference take 4 lines to write, assuming a pandas dataframe df that contains the data:

```
import dowhy
from dowhy.do_why import CausalModel
import dowhy.datasets
# Load some sample data
data=dowhy.datasets.linear_dataset(
beta=10,
num_common_causes=5,
num_instruments = 2,
num_samples=10000,
treatment_is_binary=True)
```

DoWhy supports two formats for providing the causal graph: gml (preferred) and dot. After loading in the data, we use the four main operations in DoWhy: *model*,
*estimate*, *identify* and *refute*:

```
# Create a causal model from the data and given graph.
model=CausalModel(
data = data["df"],
treatment=data["treatment_name"],
outcome=data["outcome_name"],
graph=data["dot_graph"],
)
# Identify causal effect and return target estimands
identified_estimand = model.identify_effect()
# Estimate the target estimand using a statistical method.
estimate = model.estimate_effect(identified_estimand,
method_name="backdoor.propensity_score_matching")
# Refute the obtained estimate using multiple robustness checks.
refute_results=model.refute_estimate(identified_estimand, estimate,
method_name="random_common_cause")
```

DoWhy stresses on interpretability of its output. At any point in the analysis, you can inspect the untested assumptions, identified estimands (if any) and the estimate (if any). Here’s a sample output of the linear regression estimator.

For detailed code examples, check out causalinference.gitlab.io/dowhy.

## Installation¶

**Requirements**

DoWhy support Python 3+. It requires the following packages:

- numpy
- scipy
- scikit-learn
- pandas
- networkx (for analyzing causal graphs)
- matplotlib (for general plotting)
- sympy (for rendering symbolic expressions)

Install DoWhy and its dependencies by running this from the top-most folder of the repo:

```
python setup.py install
```

- If you face any problems, try installing dependencies manually::
- pip install -r requirements.txt

Optionally, if you wish to input graphs in the dot format, then install pydot (or pygraphviz).

For better-looking graphs, you can optionally install pygraphviz. To proceed, first install graphviz and then pygraphviz (on Ubuntu and Ubuntu WSL).:

```
sudo apt install graphviz libgraphviz-dev graphviz-dev pkg-config
## from https://github.com/pygraphviz/pygraphviz/issues/71
pip install pygraphviz --install-option="--include-path=/usr/include/graphviz" \
--install-option="--library-path=/usr/lib/graphviz/"
```

Keep in mind that pygraphviz installation can be problematic on the latest versions of Python3. Tested to work with Python 3.5.

## Graphical Models and Potential Outcomes: Best of both worlds¶

DoWhy builds on two of the most powerful frameworks for causal inference: graphical models and potential outcomes. It uses graph-based criteria and do-calculus for modeling assumptions and identifying a non-parametric causal effect. For estimation, it switches to methods based primarily on potential outcomes.

## A unifying language for causal inference¶

DoWhy is based on a simple unifying language for causal inference. Causal inference may seem tricky, but almost all methods follow four key steps:

- Model a causal inference problem using assumptions.
- Identify an expression for the causal effect under these assumptions (“causal estimand”).
- Estimate the expression using statistical methods such as matching or instrumental variables.
- Finally, verify validity of the estimate using a variety of robustness checks.

This workflow can be captured by four key verbs in DoWhy:

- model
- identify
- estimate
- refute

Using these verbs, DoWhy implements a causal inference engine that can support
a variety of methods. *model* encodes prior knowledge as a formal causal graph, *identify* uses
graph-based methods to identify causal effect, *estimate* uses
statistical methods for estimating the identified estimand, and finally *refute*
tries to refute the obtained estimate by testing robustness to assumptions.

DoWhy brings three key differences compared to available software for causal inference:

**Explicit identifying assumptions**Assumptions are first-class citizens in DoWhy.

Each analysis starts with a building a causal model. The assumptions can be viewed graphically or in terms of conditional independence statements. Wherever possible, DoWhy can also automatically test for stated assumptions using observed data.

**Separation between identification and estimation**Identification is the causal problem. Estimation is simply a statistical problem.

DoWhy respects this boundary and treats them separately. This focuses the causal inference effort on identification, and frees up estimation to use any available statistical estimator for a target estimand. In addition, multiple estimation methods can be used for a single identified_estimand and vice-versa.

**Automated robustness checks**What happens when key identifying assumptions may not be satisfied?

The most critical, and often skipped, part of causal analysis is checking the robustness of an estimate to unverified assumptions. DoWhy makes it easy to automatically run sensitivity and robustness checks on the obtained estimate.

Finally, DoWhy is easily extensible, allowing other implementations of the four verbs to co-exist (we hope to integrate with external implementations in the future). The four verbs are mutually independent, so their implementations can be combined in any way.

Below are more details about the current implementation of each of these verbs.

## Model a causal problem¶

DoWhy creates an underlying causal graphical model for each problem. This serves to make each causal assumption explicit. This graph need not be complete—you can provide a partial graph, representing prior knowledge about some of the variables. DoWhy automatically considers the rest of the variables as potential confounders.

Currently, DoWhy supports two formats for graph input: gml (preferred) and dot. We strongly suggest to use gml as the input format, as it works well with networkx. You can provide the graph either as a .gml file or as a string. If you prefer to use dot format, you will need to install additional packages (pydot or pygraphviz, see installation section above). Both .dot files and string format are supported.

While not recommended, you can also specify common causes and/or instruments directly instead of providing a graph.

## Identify a target estimand under the model¶

Based on the causal graph, DoWhy finds all possible ways of identifying a desired causal effect based on the graphical model. It uses graph-based criteria and do-calculus to find potential ways find expressions that can identify the causal effect.

## Estimate causal effect based on the identified estimand¶

DoWhy supports methods based on both back-door criterion and instrumental variables. It also provides a non-parametric permutation test for testing statistical significance of obtained estimate.

Currently supported back-door criterion methods.

- Methods based on estimating the treatment assignment
- Propensity-based Stratification
- Propensity Score Matching
- Inverse Propensity Weighting

- Methods based on estimating the response surface
- Regression

Currently supported methods based on instrumental variables.

- Binary Instrument/Wald Estimator
- Regression discontinuity

## Refute the obtained estimate¶

Having access to multiple refutation methods to verify a causal inference is a key benefit of using DoWhy.

DoWhy supports the following refutation methods.

- Placebo Treatment
- Irrelevant Additional Confounder
- Subset validation

## Contributing¶

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

- DoWhy | Making causal inference easy
- The need for causal inference
- Sample causal inference analysis in DoWhy
- Installation
- Graphical Models and Potential Outcomes: Best of both worlds
- A unifying language for causal inference
- Model a causal problem
- Identify a target estimand under the model
- Estimate causal effect based on the identified estimand
- Refute the obtained estimate
- Contributing

- Getting started with DoWhy: A simple example
- DoWhy: Different estimation methods for causal inference
- Different ways to load an input graph
- Confounding Example: Finding causal effects from observed data
- Let’s create a mystery dataset. Need to find if there is a causal effect.
- Using DoWhy to resolve the mystery:
*Does Treatment cause Outcome variable?* - DoWhy example on the Lalonde dataset
- Code repository
- dowhy package
- Subpackages
- dowhy.causal_estimators package
- Submodules
- dowhy.causal_estimators.instrumental_variable_estimator module
- dowhy.causal_estimators.linear_regression_estimator module
- dowhy.causal_estimators.propensity_score_matching_estimator module
- dowhy.causal_estimators.propensity_score_stratification_estimator module
- dowhy.causal_estimators.propensity_score_weighting_estimator module
- dowhy.causal_estimators.regression_discontinuity_estimator module
- Module contents

- dowhy.causal_refuters package

- dowhy.causal_estimators package
- Submodules
- dowhy.causal_estimator module
- dowhy.causal_graph module
- dowhy.causal_identifier module
- dowhy.causal_refuter module
- dowhy.data_transformer module
- dowhy.datasets module
- dowhy.do_why module
- dowhy.plotter module
- Module contents

- Subpackages
- dowhy