Implemented Tables.jl API and support for GPUs

Author: dscolby

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.11.1"
 manifest_format = "2.0"
-project_hash = "ef638d9b7dd3411a6b5c86406ac77e48f19d8d42"
+project_hash = "48b0ecc3de09367019241b9866f1be8d1ab8f4cc"
 
 [[deps.Artifacts]]
 uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
@@ -13,6 +13,21 @@ deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
 version = "1.1.1+0"
 
+[[deps.DataAPI]]
+git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
+uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
+version = "1.16.0"
+
+[[deps.DataValueInterfaces]]
+git-tree-sha1 = "bfc1187b79289637fa0ef6d4436ebdfe6905cbd6"
+uuid = "e2d170a0-9d28-54be-80f0-106bbe20a464"
+version = "1.0.0"
+
+[[deps.IteratorInterfaceExtensions]]
+git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856"
+uuid = "82899510-4779-5014-852e-03e436cf321d"
+version = "1.0.0"
+
 [[deps.Libdl]]
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 version = "1.11.0"
@@ -27,6 +42,11 @@ deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
 uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
 version = "0.3.27+1"
 
+[[deps.OrderedCollections]]
+git-tree-sha1 = "12f1439c4f986bb868acda6ea33ebc78e19b95ad"
+uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
+version = "1.7.0"
+
 [[deps.Random]]
 deps = ["SHA"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
@@ -36,6 +56,18 @@ version = "1.11.0"
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 version = "0.7.0"
 
+[[deps.TableTraits]]
+deps = ["IteratorInterfaceExtensions"]
+git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
+uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
+version = "1.0.1"
+
+[[deps.Tables]]
+deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "OrderedCollections", "TableTraits"]
+git-tree-sha1 = "598cd7c1f68d1e205689b1c2fe65a9f85846f297"
+uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
+version = "1.12.0"
+
 [[deps.libblastrampoline_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"

Project.toml

@@ -6,13 +6,15 @@ version = "0.8.0"
 [deps]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
 
 [compat]
 Aqua = "0.8"
 DataFrames = "1.5"
 Documenter = "1.2"
 LinearAlgebra = "1.8"
 Random = "1.8"
+Tables = "1.12.0"
 Test = "1.8"
 julia = "1.8"
 

docs/src/api.md

@@ -112,4 +112,5 @@ CausalELM.clip_if_binary
 CausalELM.@model_config
 CausalELM.@standard_input_data
 CausalELM.generate_folds
+CausalELM.convert_if_table
 ```

docs/src/guide/doublemachinelearning.md

@@ -16,9 +16,9 @@ the residuals from the first stage models.
 
 ## Step 1: Initialize a Model
 The DoubleMachineLearning constructor takes at least three arguments—covariates, a 
-treatment statuses, and outcomes, all of which may be either an array or any struct that 
-implements the Tables.jl interface (e.g. DataFrames). This estimator supports binary, count, 
-or continuous treatments and binary, count, continuous, or time to event outcomes.
+treatment statuses, and outcomes, all of which may be either an AbstractArray or any struct 
+that implements the Tables.jl interface (e.g. DataFrames). This estimator supports binary, 
+count, or continuous treatments and binary, count, continuous, or time to event outcomes.
 
 !!! note
     Non-binary categorical outcomes are treated as continuous.

docs/src/guide/gcomputation.md

@@ -16,9 +16,9 @@ steps for using G-computation in CausalELM are below.
 
 ## Step 1: Initialize a Model
 The GComputation constructor takes at least three arguments: covariates, treatment statuses, 
-outcomes, all of which can be either an array or any data structure that implements the 
-Tables.jl interface (e.g. DataFrames). This implementation supports binary treatments and 
-binary, continuous, time to event, and count outcome variables.
+outcomes, all of which can be either an AbstractArray or any data structure that implements 
+the Tables.jl interface (e.g. DataFrames). This implementation supports binary treatments 
+and binary, continuous, time to event, and count outcome variables.
 
 !!! note
     Non-binary categorical outcomes are treated as continuous.

docs/src/guide/its.md

@@ -31,7 +31,7 @@ Estimating an interrupted time series design in CausalELM consists of three step
 ## Step 1: Initialize an interrupted time series estimator
 The InterruptedTimeSeries constructor takes at least four agruments: pre-event covariates, 
 pre-event outcomes, post-event covariates, and post-event outcomes, all of which can be 
-either an array or any data structure that implements the Tables.jl interface (e.g. 
+either an AbstractArray or any data structure that implements the Tables.jl interface (e.g. 
 DataFrames). The interrupted time series estimator assumes outcomes are either continuous, 
 count, or time to event variables.
 

docs/src/guide/metalearners.md

@@ -30,13 +30,14 @@ continuous outcomes.
         Kennedy, Edward H. "Towards optimal doubly robust estimation of heterogeneous causal 
         effects." Electronic Journal of Statistics 17, no. 2 (2023): 3008-3049.
 
-# Initialize a Metalearner
+# Step 1: Initialize a Metalearner
 S-learners, T-learners, X-learners, R-learners, and doubly robust estimators all take at 
 least three arguments—covariates, treatment statuses, and outcomes, all of which can be 
-either an array or any struct that implements the Tables.jl interface (e.g. DataFrames). S, 
-T, X, and doubly robust learners support binary treatment variables and binary, continuous, 
-count, or time to event outcomes. The R-learning estimator supports binary, continuous, or 
-count treatment variables and binary, continuous, count, or time to event outcomes.
+either an AbstractArray or any struct that implements the Tables.jl interface (e.g. 
+DataFrames). S, T, X, and doubly robust learners support binary treatment variables and 
+binary, continuous, count, or time to event outcomes. The R-learning estimator supports 
+binary, continuous, or count treatment variables and binary, continuous, count, or time to 
+event outcomes.
 
 !!! note
     Non-binary categorical outcomes are treated as continuous.
@@ -64,7 +65,7 @@ r_learner = RLearner(X, Y, T)
 dr_learner = DoublyRobustLearner(X, T, Y)
 ```
 
-# Estimate the CATE
+# Step 2: Estimate the CATE
 We can estimate the CATE for all the models by passing them to estimate_causal_effect!.
 ```julia
 estimate_causal_effect!(s_learner)
@@ -74,7 +75,7 @@ estimate_causal_effect!(r_learner)
 estimate_causal_effect!(dr_lwarner)
 ```
 
-# Get a Summary
+# Step 3: Get a Summary
 We can get a summary of the model by pasing the model to the summarize method.
 
 !!!note

docs/src/index.md

@@ -75,7 +75,7 @@ these libraries are:
     econometrics, and biostatistics.
 
 ### Installation
-CausalELM requires Julia version 1.7 or greater and can be installed from the REPL as shown 
+CausalELM requires Julia version 1.8 or greater and can be installed from the REPL as shown 
 below. 
 ```julia
 using Pkg 

docs/src/release_notes.md

@@ -5,11 +5,13 @@ These release notes adhere to the [keep a changelog](https://keepachangelog.com/
 ### Added
 *   Implemented randomization inference-based confidence intervals [#78](https://github.com/dscolby/CausalELM.jl/issues/78)
 *   Added marginal effects to model summaries [#78](https://github.com/dscolby/CausalELM.jl/issues/78)
+*   CausalELM models now support any AbstractArray data type, including support for using GPUs with CuArrays or similar structures for Mac, Intel, and AMD hardware[#37](https://github.com/dscolby/CausalELM.jl/issues/37)
 ### Fixed
 *   Removed unnecessary include and using statements
 *   Slightly sped up the randomization inference implementation and clarified it in the docs [#77](https://github.com/dscolby/CausalELM.jl/issues/77)
 *   Fixed the randomization inference index selection procedure for interrupted time series estimators
 *   Inlined certain methods to slightly improve performance [#76](https://github.com/dscolby/CausalELM.jl/issues/76)
+*   CausalELM models now support any data structure that implements the Tables.jl API, not just DataFrames
 
 ## Version [v0.7.0](https://github.com/dscolby/CausalELM.jl/releases/tag/v0.7.0) - 2024-06-22
 ### Added

src/estimators.jl

@@ -7,10 +7,14 @@ abstract type CausalEstimator end
 Initialize an interrupted time series estimator. 
 
 # Arguments
-- `X₀::Any`: array or DataFrame of covariates from the pre-treatment period.
-- `Y₁::Any`: array or DataFrame of outcomes from the pre-treatment period.
-- `X₁::Any`: array or DataFrame of covariates from the post-treatment period.
-- `Y₁::Any`: array or DataFrame of outcomes from the post-treatment period.
+- `X₀::Any`: AbstractArray or Tables.jl API compliant data structure of covariates from the 
+    pre-treatment period.
+- `Y₁::Any`: AbstractArray or Tables.jl API compliant data structure of outcomes from the 
+    pre-treatment period.
+- `X₁::Any`: AbstractArray or Tables.jl API compliant data structure of covariates from the 
+    post-treatment period.
+- `Y₁::Any`: AbstractArray or Tables.jl API compliant data structure of outcomes from the 
+    post-treatment period.
 
 # Keywords
 - `activation::Function=swish`: activation function to use.
@@ -44,10 +48,10 @@ julia> m3 = InterruptedTimeSeries(x₀_df, y₀_df, x₁_df, y₁_df)
 ```
 """
 mutable struct InterruptedTimeSeries
-    X₀::Array{Float64}
-    Y₀::Array{Float64}
-    X₁::Array{Float64}
-    Y₁::Array{Float64}
+    X₀::AbstractArray{<: Real}
+    Y₀::AbstractArray{<: Real}
+    X₁::AbstractArray{<: Real}
+    Y₁::AbstractArray{<: Real}
     marginal_effect::Float64
     @model_config individual_effect
 end
@@ -65,7 +69,7 @@ function InterruptedTimeSeries(
     autoregression::Bool=true,
 )
     # Convert to arrays
-    X₀, X₁, Y₀, Y₁ = Matrix{Float64}(X₀), Matrix{Float64}(X₁), Y₀[:, 1], Y₁[:, 1]
+    X₀, X₁, Y₀, Y₁ = convert_if_table.((X₀, X₁, Y₀, Y₁))
 
     # Add autoregressive term
     X₀ = ifelse(autoregression == true, reduce(hcat, (X₀, moving_average(Y₀))), X₀)
@@ -97,9 +101,9 @@ end
 Initialize a G-Computation estimator.
 
 # Arguments
-- `X::Any`: array or DataFrame of covariates.
-- `T::Any`: vector or DataFrame of treatment statuses.
-- `Y::Any`: array or DataFrame of outcomes.
+- `X::Any`: AbstractArray or Tables.jl API compliant data structure of covariates.
+- `T::Any`: AbstractArray or Tables.jl API compliant data structure of treatment statuses.
+- `Y::Any`: AbstractArray or Tables.jl API compliant data structure of outcomes.
 
 # Keywords
 - `quantity_of_interest::String`: ATE for average treatment effect or ATT for average 
@@ -159,7 +163,7 @@ mutable struct GComputation <: CausalEstimator
         end
 
         # Convert to arrays
-        X, T, Y = Matrix{Float64}(X), T[:, 1], Y[:, 1]
+        X, T, Y = convert_if_table.((X, T, Y))
 
         task = var_type(Y) isa Binary ? "classification" : "regression"
 
@@ -187,9 +191,10 @@ end
 Initialize a double machine learning estimator with cross fitting.
 
 # Arguments
-- `X::Any`: array or DataFrame of covariates of interest.
-- `T::Any`: vector or DataFrame of treatment statuses.
-- `Y::Any`: array or DataFrame of outcomes.
+- `X::Any`: AbstractArray or Tables.jl API compliant data structure of covariates of
+    interest.
+- `T::Any`: AbstractArray or Tables.jl API compliant data structure of treatment statuses.
+- `Y::Any`: AbstractArray or Tables.jl API compliant data structure of outcomes.
 
 # Keywords
 - `activation::Function=swish`: activation function to use.
@@ -240,7 +245,7 @@ function DoubleMachineLearning(
     folds::Integer=5,
 )
     # Convert to arrays
-    X, T, Y = Matrix{Float64}(X), T[:, 1], Y[:, 1]
+    X, T, Y = convert_if_table.((X, T, Y))
 
     # Shuffle data with random indices
     indices = shuffle(1:length(Y))