CausalTables.jl

A package for storing and simulating data for causal inference in Julia.

Given a dataset, methods for statistical causal inference evaluate how an intervention on some treatment variable $A$ would affect a response variable $Y$, typically in the presence of other potential confounding variables. CausalTables.jl is a tool for to evaluating and compare the statistical performance of causal inference methods in Julia. The package provides:

1. A CausalTable data structure that wraps any dataset stored in a Tables.jl-compatible format with a causal structure (i.e. labeling treatment, response, confounders, and other variables) for use by external causal inference packages.
2. A StructuralCausalModel data structure allowing users to easily generate random datasets endowed with a given causal structure.
3. Methods to computing true conditional distributions and approximate ground truth causal effect estimands for a given StructuralCausalModel

When used together, both functionalities allow users to benchmark methods for statistical causal inference in Julia -- that is, evaluate the performance of new and existing causal inference methods against both each other and the the ground truth.

Installation

CausalTables.jl can be installed using the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run

Pkg> add CausalTables

Running a simulation with CausalTables.jl

To simulate data, one must first define a StructuralCausalModel (SCM). An SCM is composed of a DataGeneratingProcess, which is a sequence of random variables, along with labels for treatment, response, and confounder variables. For example, the following code defines an SCM with a binary treatment $A$, a continuous confounder $W$, and a continuous response $Y$. The @dgp macro constructs a DataGeneratingProcess object according to the simple syntax name ~ distribution, where rhs is a Distribution object from Distributions.jl. More advanced syntax is detailed in the documentation.

using CausalTables
using Distributions

dgp = @dgp(
    W ~ Beta(2, 4),
    A ~ (@. Bernoulli(W)),
    Y ~ (@. Normal(A + W, 1))
)

scm = StructuralCausalModel(dgp; treatment = :A, response = :Y, confounders = [:W])

Once a StructuralCausalModel is defined, one can then draw a randomly-generated CausalTable according to the SCM using the rand function:

ctbl = rand(scm, 100)

CausalTable
┌───────────┬───────┬───────────┐
│         W │     A │         Y │
│   Float64 │  Bool │   Float64 │
├───────────┼───────┼───────────┤
│  0.381527 │  true │  0.809227 │
│  0.576206 │  true │   3.22163 │
│  0.380546 │ false │   1.70505 │
│  0.226648 │ false │  0.185022 │
│     ⋮     │   ⋮   │     ⋮     │
│  0.385836 │ false │ -0.392848 │
│  0.204554 │ false │  0.638084 │
│  0.232177 │  true │  0.832707 │
│ 0.0465189 │ false │   1.29168 │
└───────────┴───────┴───────────┘
                  92 rows omitted
Summaries: NamedTuple()
Arrays: NamedTuple()

A CausalTable is a Table with a causal structure, such as labels for treatment, response, and confounder variables. In addition to implementing the standard Tables.jl interface, CausalTables.jl also provides extra functions to make working with causal data easier. See the documentation for more information.

Given an SCM, it is also possible to approximate the "ground truth" value of a variety of relevant causal estimands from this SCM, including counterfactual means (cfmean), as well as average treatment effects (ate) and average policy effects (ape). For example, the ground truth average treatment effect for this SCM can be approximated like so:

ate(scm)

(μ = 1.0006736394005957, eff_bound = 2.0019300075151616)

Alternatively, one can compute the ground truth of low-level statistical functionals, such as conditional means or propensity scores, for use in downstream analyses.

propensity(scm, ctbl, :A)

100-element Vector{Float64}:
 0.17900559797871887
 0.653489070854697
 ⋮
 0.8247576289722464

See the documentation for more information and tutorials.

Community Guidelines

If you find a bug, have a feature request, or otherwise experience any issues with this software package, please open an issue on the issue tracker. If you would like to contribute code to the software yourself, we encourage you to open a pull request. We welcome all contributions, including bug fixes, documentation improvements, and new features.