InvariantCausal.jl

Causal Inference with Invariant Prediction

This is a Julia 1.x implementation for the Invariant Causal Prediction algorithm of Peters, Bühlmann and Meinshausen. The method uncovers direct causes of a target variable from datasets under different environments (e.g., interventions or experimental settings).

See also this R package and this report.

Changelog

2020/12/03: version 1.0.0 (Julia 1.x)
2018/06/20: version 0.1.1 (Julia 0.6)

Dependencies

DataStructures.jl, StatsBase.jl, GLM.jl, DataFrames.jl, GLMNet.jl (for lasso screening and requires gfortran) and UnicodePlots.jl.

Installation

Install the package via typing the following in Julia REPL.

julia> using Pkg
julia> Pkg.add("InvariantCausal")

Alternatively, you can install the latest from GitHub.

julia> Pkg.add(url="https://github.com/richardkwo/InvariantCausal.git")

Use the following to run a full test.

julia> using InvariantCausal
julia> InvariantCausal._test_full()

Quick Start

Generate a simple Gaussian structure equation model (SEM) with random graph with 21 variables and average degree 3. Note that we assume the SEM is acyclic. The model can be represented as X = B X + ϵ with zeros on the diagonals of B (no self-loop). ϵ is a vector of independent Gaussian errors. For a variable i, variables j with coefficients B[i,j] non-zero are called the direct causes of i. We assume B is sparse, and its sparsity pattern is visualized with UnicodePlots.jl.

julia> using InvariantCausal
julia> using Random
julia> Random.seed!(77)
julia> sem_obs = random_gaussian_SEM(21, 3)

Gaussian SEM with 21 variables:
B =
      Sparsity Pattern
      ┌───────────┐
    1 │⠀⠠⠀⠀⢐⠀⠀⠄⠀⢔⠀│ > 0
      │⠠⠀⠠⠨⠁⠀⠄⠀⠀⠸⠀│ < 0
      │⠠⠈⠈⠀⠌⠠⠀⠅⠀⠩⠉│
      │⠠⣨⠴⠰⠪⠠⠄⠀⠸⠉⣐│
      │⢀⠲⠈⢠⠠⠀⠀⠂⠀⠲⠁│
   21 │⠀⠐⠀⠀⠠⠠⠀⠀⠀⠔⠀│
      └───────────┘
      1          21
        nz = 70σ² = [1.9727697778060356, 1.1224733663047743, 1.1798805640594814, 1.2625825149076064, 0.8503782631176267, 0.5262963446298372, 1.3835334059064883, 1.788996301274282, 1.759286517329432, 0.842571682652995, 1.713382150423666, 1.4524484793202235, 1.9464648511794784, 1.7729995603828317, 0.7110857327642559, 1.6837378902964577, 1.085405687408806, 1.3069888003095986, 1.3933773717634643, 1.0571823834646068, 1.9187793877731028]

Suppose we want to infer the direct causes for the last variables, i.e., 9, 11 and 18.

julia> causes(sem_obs, 21)
3-element Array{Int64,1}:
  9
 11
 18

Firstly, let us generate some observational data and call it environment 1.

julia> X1 = simulate(sem_obs, 1000)

Then, we simulate from environment 2 by performing do-intervention on variables 3, 4, 5, 6. Here we set them to fixed random values.

julia> X2 = simulate(sem_obs, [3,4,5,6], randn(4), 1000)

We run the algorithm on environments 1 and 2.

julia> causalSearch(vcat(X1, X2)[:,1:20], vcat(X1, X2)[:,21], repeat([1,2], inner=1000))

8 variables are screened out from 20 variables with lasso: [5, 7, 8, 9, 11, 12, 15, 17]
Causal invariance search across 2 environments with at α=0.01 (|S| = 8, method = chow, model = linear)

S = []                                      : p-value = 0.0000 [ ] ⋂ = [5, 7, 8, 9, 11, 12, 15, 17]
S = [5]                                     : p-value = 0.0000 [ ] ⋂ = [5, 7, 8, 9, 11, 12, 15, 17]
S = [17]                                    : p-value = 0.0000 [ ] ⋂ = [5, 7, 8, 9, 11, 12, 15, 17]
S = [15]                                    : p-value = 0.0000 [ ] ⋂ = [5, 7, 8, 9, 11, 12, 15, 17]
S = [12]                                    : p-value = 0.0000 [ ] ⋂ = [5, 7, 8, 9, 11, 12, 15, 17]
S = [11]                                    : p-value = 0.0144 [*] ⋂ = [11]
S = [9]                                     : p-value = 0.0000 [ ] ⋂ = [11]
S = [8]                                     : p-value = 0.0000 [ ] ⋂ = [11]
S = [7]                                     : p-value = 0.0000 [ ] ⋂ = [11]
S = [11, 5]                                 : p-value = 0.0000 [ ] ⋂ = [11]
S = [11, 12]                                : p-value = 0.0000 [ ] ⋂ = [11]
S = [11, 15]                                : p-value = 0.0007 [ ] ⋂ = [11]
S = [7, 11]                                 : p-value = 0.0082 [ ] ⋂ = [11]
S = [11, 8]                                 : p-value = 0.0000 [ ] ⋂ = [11]
S = [9, 11]                                 : p-value = 0.0512 [*] ⋂ = [11]
S = [17, 11]                                : p-value = 0.0000 [ ] ⋂ = [11]
S = [9, 12]                                 : p-value = 0.0000 [ ] ⋂ = [11]
S = [9, 15]                                 : p-value = 0.0064 [ ] ⋂ = [11]
S = [7, 9]                                  : p-value = 0.0000 [ ] ⋂ = [11]
S = [9, 8]                                  : p-value = 0.0000 [ ] ⋂ = [11]
S = [9, 5]                                  : p-value = 0.7475 [*] ⋂ = Int64[]

Tested 21 sets: 3 sets are accepted.

 * Found no causal variable (empty intersection).

 ⋅ Variables considered include [5, 7, 8, 9, 11, 12, 15, 17]

The algorithm cannot find any direct causal variables (parents) of variable 21 due to insufficient power of two environments. The algorithm tends to discover more with more environments. Let us define a new environment where we perform a noise (soft) intervention that changes the equations for 5 variables other than the target. Note it is important that the target is left untouched.

julia> sem_noise, variables_intervened = random_noise_intervened_SEM(sem_obs, p_intervened=5, avoid=[21])

(Gaussian SEM with 21 variables:
B =
      Sparsity Pattern
      ┌───────────┐
    1 │⠀⠠⠀⠀⢐⠀⠀⠄⠀⢔⠀│ > 0
      │⠠⠀⠠⠨⠁⠀⠄⠀⠀⠸⠀│ < 0
      │⠠⠈⠈⠀⠌⠠⠀⠅⠀⠩⠉│
      │⠠⣨⠴⠰⠪⠠⠄⠀⠸⠉⣐│
      │⢀⠲⠈⢠⠠⠀⠀⠂⠀⠲⠁│
   21 │⠀⠐⠀⠀⠠⠠⠀⠀⠀⠔⠀│
      └───────────┘
      1          21
        nz = 70σ² = [1.9727697778060356, 1.1224733663047743, 1.1798805640594814, 1.2625825149076064, 0.8503782631176267, 0.5262963446298372, 1.3835334059064883, 1.788996301274282, 1.759286517329432, 0.5837984015051159, 3.01957479564807, 0.9492838187140921, 1.9398913901673531, 1.7729995603828317, 0.7110857327642559, 1.6837378902964577, 1.2089053651343495, 1.3069888003095986, 1.3933773717634643, 1.0571823834646068, 1.9187793877731028], [17, 13, 10, 11, 12])

Here the equations for variables 17, 13, 10, 11, 12 have been changed. Now we simulate from this modified SEM and call it environment 3. We run the algorithm on all 3 environments.

julia> X3 = simulate(sem_noise, 1000)
julia> causalSearch(vcat(X1, X2, X3)[:,1:20], vcat(X1, X2, X3)[:,21], repeat([1,2,3], inner=1000))

The algorithm searches over subsets for a while and successfully discovers variables 11. The other two causes, 9 and 18, can hopefully be discovered given even more environments.

causalSearch(vcat(X1, X2, X3)[:,1:20], vcat(X1, X2, X3)[:,21], repeat([1,2,3], inner=1000))
8 variables are screened out from 20 variables with lasso: [4, 5, 7, 8, 9, 11, 12, 16]
Causal invariance search across 3 environments with at α=0.01 (|S| = 8, method = chow, model = linear)

S = []                                      : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [4]                                     : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [16]                                    : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [12]                                    : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [11]                                    : p-value = 0.0084 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [9]                                     : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [8]                                     : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [7]                                     : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [5]                                     : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [4, 11]                                 : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [11, 5]                                 : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [11, 8]                                 : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [7, 11]                                 : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [9, 11]                                 : p-value = 0.0000 [ ] ⋂ = [4, 5, 7, 8, 9, 11, 12, 16]
S = [16, 11]                                : p-value = 0.0709 [*] ⋂ = [11, 16]
S = [11, 12]                                : p-value = 0.0000 [ ] ⋂ = [11, 16]
																			...
S = [7, 9, 4, 16, 11, 5, 12]                : p-value = 0.0000 [ ] ⋂ = [11]
S = [7, 9, 4, 16, 11, 8, 12]                : p-value = 0.0001 [ ] ⋂ = [11]
S = [7, 4, 9, 16, 11, 5, 8, 12]             : p-value = 0.0002 [ ] ⋂ = [11]

Tested 256 sets: 6 sets are accepted.

 * Causal variables include: [11]

   variable   	 1.0 % 		 99.0 %
   11         	 0.1123 	 1.1017

 ⋅ Variables considered include [4, 5, 7, 8, 9, 11, 12, 16]

Functionalities

The main algorithm causalSearch(X, y, env, [S]; α=0.01, method="chow", screen="auto", p_max=8, verbose=true, selection_only=false, n_max_for_exact=5000)
- Performs screening if number of covariates exceeds p_max
  - screen="auto": "HOLP" when p > n, "lasso" otherwise
  - screen="HOLP": High dimensional ordinary least squares projection for screening variables when p ≧ n
  - screen="lasso": lasso solution path from glmnet
- Skips supersets of an accepted set under selection_only = true, but confidence intervals are not reported
- When sample size exceeds n_max_for_exact, sub-sampling is used for Chow test
Methods
- method="chow": Chow test for linear regression
- method="logistic-LR": likelihood-ratio test for logistic regression
- method="logistic-SF": Sukhatme-Fisher test for testing equal mean and variance of logistic prediction residuals
SEM utilities: random_gaussian_SEM, random_noise_intervened_SEM, simulate, causes and cov for generating random SEM (Erdos-Renyi), simulation and interventions.
Variables screening:
- Lasso (with glmnet): screen_lasso(X, y, pmax)

Features

High performance implementation in Julia v1.x
Faster search:
- skipping testing supersets of A if A is accepted ( under selection_only mode)
- Priority queue to prioritize testing sets likely to be invariant