v1.2.2

CausalTables v1.2.2

Diff since v1.2.1

New Features

• Fixed typos and tagline in documentation, README, and paper
• Preparation for JOSS

v1.2.1

CausalTables v1.2.1

Diff since v1.2.0

New Features

• Changed replace to Base.replace to avoid errors when both are used in other packages.

Merged pull requests:

• Release 1.2.1 (#38) (@salbalkus)

v1.2.0

CausalTables v1.2.0

Diff since v1.1.1

New Features

• CausalTables are now constructed with a causes parameter instead of confounders. This permits treatment-response pairs with different sets of confounders and supports the extraction of causal-relevant variables such as confounders, mediators, and instruments algorithmically based on the listed causes of each treatment and response. This will break existing code that specified confounders for a CausalTable using the confounders parameter.
• Added mediators and instruments along with corresponding "name" and "matrix" versions to extract mediators and instrumental variables from a given CausalTable.
• Added convenience constructors to make it easier to create DataGeneratingProcess objects with default names and labels.
• Added merge function to combine two DataGeneratingProcess objects into one.
• Can now call rand(scm) without specifying sample size for settings where the SCM is "data-adaptive" in the sense that parameters other than the first distribution within the DGP depend on the sample size.

Merged pull requests:

• Release 1.2.0 (#37) (@salbalkus)

v1.1.1

CausalTables v1.1.1

Diff since v1.1.0

New Features

• Added parents function, which subsets the columns of a CausalTable to only those labeled as causes of a given variable.
• Updated paper.

Merged pull requests:

• Release 1.1.1 (#36) (@salbalkus)

Closed issues:

• Documentation Review (#20)

v1.1.0

CausalTables v1.1.0

Diff since v1.0.0

New Features

• Added intervene function to intervene on the treatment of an existing CausalTable. Currently implemented interventions include treat_all and treat_none (for binary treatments) as well as additive_mtp(δ) and multiplicative_mtp(δ) for continuous treatments. New interventions coming soon!
• Added propensity function to compute (generalized) propensity scores for a given variable in a CausalTable.
• Added responsematrix, treatmentmatrix, and confoundersmatrix functions for getting the response, treatment, and confounder variables in matrix form.
• Updated paper and documentation.

Merged pull requests:

• Release 1.1.0 (#35) (@salbalkus)

v1.0.0

CausalTables v1.0.0

The first stable release of CausalTables.jl. Implements the CausalTable, DataGeneratingProcess, and StructuralCausalModel data structures, along with associated methods. These support easy wrapping and simulating data with labeled causal structure.