Description:

chirho 0.2.0

Causal Reasoning with ChiRho
ChiRho is a causal extension to the Pyro probabilistic programming
language. It was built to bridge the gap between the capabilities of
modern probabilistic programming systems, such as Pyro, and the needs of
policymakers, scientists, and AI researchers, who often want to use
models to answer their questions about cause-and-effect relationships.
As a non-exhaustive set of examples, ChiRho makes it easier to
answer the following kinds of causal questions that appear frequently in
practice.

Interventional: How many COVID-19 hospitalizations will occur if
the USA imposes a national mask mandate?
Counterfactual: Given that 100,000 people were infected with
COVID-19 in the past month, how many would have been infected if a
mask mandate had been in place?
Explanation: Why were 100,000 people infected with COVID-19 in
the past month?
Causal structure discovery: What individual attributes influence
risk of COVID-19 hospitalization?

To see how ChiRho supports causal reasoning, take a look at our Tutorial.

Installation
Install using pip:
pip install chirho
Install from source:
git clone git@github.com:BasisResearch/chirho.git
cd chirho
git checkout master
pip install .
Install with extra packages:
To install the dependencies required to run the tutorials in
examples/tutorials directories, please use the following
command:
pip install chirho[extras]
Make sure that the models come from the same release version of the
ChiRho source
code as you
have installed.


Getting Started
Below is a simple example of how to use ChiRho to answer an
interventional question. For more in-depth examples, go to Learn
more.
import torch
import pyro
import pyro.distributions as dist
from chirho.interventional.handlers import do

pyro.set_rng_seed(101)

# Define a causal model with single confounder h
def model():
h = pyro.sample("h", dist.Normal(0, 1))
x = pyro.sample("x", dist.Normal(h, 1))
y = pyro.sample("y", dist.Normal(x + h, 1))
return y

# Define a causal query (here intervening on x)
def queried_model():
return do(model, {"x": 1})

# Generate 10,000 samples from the observational distribution P(y) ~ N(0, 2)
obs_samples = pyro.infer.Predictive(model, num_samples=1000)()["y"]

# Generate 10,000 samples from the interventional distribution P(y | do(X=1)) ~ N(1, 1)
int_samples = pyro.infer.Predictive(queried_model(), num_samples=1000)()["y"]


Learn more
We have written a number of tutorials and examples for ChiRho. We
have tried to choose simple examples that would be of interest to both
the causal inference and probabilistic programming communities: they
collectively span Pearl’s causal hierarchy (Pearl 2009), and
most are broadly applicable, empirically validated, have an
unconventional or limited identification result, and make use of modern
probabilistic machine learning tools, like neural networks or stochastic
variational inference.
Our examples demonstrate how real-world causal assumptions can be expressed as probabilistic programs
and real-world causal estimands can be expressed as program transformations.
These example illustrate how ChiRho is compatible with any inference method
implemented in Pyro, including the kinds of scalable gradient-based
approximations that power much of the modern probabilistic machine learning landscape.

Tutorial
- Key observations inspiring ChiRho’s design and outlines a causal Bayesian workflow for using ChiRho to answer causal questions
Example: Backdoor Adjustment Criteria
- Adjusting for observed confounders
Example: Causal Effect Variational Autoencoder
- Causal inference with deep models and proxy variables
Example: Mediation analysis and (in)direct effects
- Mediation analysis for path specific effects
Example: Deep structural causal model counterfactuals
- Counterfactuals with normalizing flows
Example: Structured Latent Confounders
- Causal effect estimation when latent confounders are shared across groups
Example: Synthetic difference-in-differences
- Counterfactual estimation from longitudinal data
Example: Robust estimation with the DR learner
- Heterogeneous causal effect estimation with a misspecified model
Example: Estimating the effects of drugs on gene expression
- Causal inference with single-cell RNA-seq data
Example: Causal reasoning in dynamical systems
- Causal inference with continuous-time dynamical systems
Design notes
- Technical implementation details of ChiRho using effect handlers

Note: These tutorials and examples assume some familiarity with Pyro and
probabilistic programming. For introductory Pyro tutorials, please see
Additional background reading
material below.


Documentation

Counterfactual
- Effect handlers for counterfactual world splitting
Interventional
- Effect handlers for performing interventions
Observational
- Effect handler utilities for computing probabilistic quantities for
partially deterministic models which is useful for counterfactual reasoning
Indexed
- Effect handler utilities for named indices in ChiRho which is useful for manipluating
and tracking counterfactual worlds
Dynamical
- Operations and effect handlers for counterfactual reasoning in dynamical systems



Caveats
ChiRho does not answer causal questions by magic. In fact, there is
no escaping the fact that

behind any causal conclusion there must lie some causal assumption,

a phrase made famous by Judea Pearl (Pearl 2009). Instead,
ChiRho provides a substrate for writing causal assumptions as
probabilistic programs, and for writing causal questions in terms of
program transformations.


Additional background reading material

Causal Probabilistic Programming Without Tears
https://drive.google.com/file/d/1Uzjg-vX77BdSnAcfpUcb-aIXxhnAPI24/view?usp=sharing
Introduction to Pyro: http://pyro.ai/examples/intro_long.html
Tensor shapes in Pyro: http://pyro.ai/examples/tensor_shapes.html
A guide to programming with effect handlers in
Pyro http://pyro.ai/examples/effect_handlers.html
Minipyro: http://pyro.ai/examples/minipyro.html
Reparameterization of Pyro
programs: https://docs.pyro.ai/en/stable/infer.reparam.html
Optional: getting started with
NumPyro https://num.pyro.ai/en/stable/getting_started.html



References
Pearl, Judea. Causality: Models, Reasoning and Inference. 2nd ed. USA: Cambridge University Press, 2009.

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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