Changelog

Source: NEWS.md

CausalQueries 1.3.0

CRAN release: 2024-12-14

This is a minor release introducing the option to specify causal queries with givens in a single statement. This new functionality is meant to make query specification more concise, expressive, and intuitive for users more comfortable with standard statistical notation for conditional distributions.

New Functionality

1. Combining queries and givens

Instead of specifying the conditioning set of a query in the given argument the given statement defining the conditioning set may now be added to the query statement directly after the :|: operator. We opt for :|: instead of the traditional | conditioning operator to avoid confusion with the built in logical or operator |.

model <- CausalQueries::make_model("X -> Y")

# using given argument
CausalQueries::query_model(model, queries = "Y[X=1] - Y[X=0]", given = "X == 1 & Y == 1")

# new combined specification option
CausalQueries::query_model(model, queries = "Y[X=1] - Y[X=0] :|: X == 1 & Y == 1")

CausalQueries 1.2.1

CRAN release: 2024-11-05

This is a minor release introducing changes meant to focus S3 methods and utility functions around two core classes: causal_model and model_query. Our aim is to improve the user experience of CausalQueries by focusing user facing functionality more clearly around the workflow of making, updating, querying and inspecting causal models. With respect to causal_model objects this release introduces more expressive and concise S3 summary and print methods for the causal_model class and its internal objects. Updates to the grab() and inspect() functions streamline access to objects contained within a causal_model, facilitating more advanced use-cases or deeper review. This release introduces the model_query class along with S3 summary, print and plot methods for a more seamless querying workflow. Finally, this release removes dependency on dagitty, restoring compatibility of CausalQueries with systems on which V8 JavaScript WASM is not supported.

New Functionality

1. Improved causal_model summaries

The summary() method for objects of class causal_model now supports an include argument allowing users to specify additional objects internal to the causal_model object for which they would like to have summaries appended to the main output of summary(). Summaries have additionally been made more informative and readable. Please see ?summary.causal_model for extensive documentation on the new functionality.

2. Streamlined causal_model object access

Internal objects of a causal_model instance can now be returned quietly via grab() eliminating the need to interact with a causal_model instance directly.

3. New querying utility functionality

The newly introduced model_query class comes with a print, summary and plot method. plot() generates a coefficient plot with credible intervals for evaluated queries.

CausalQueries 1.1.1

CRAN release: 2024-04-26

This is a patch release fixing a bug in the print.model_query() S3 method that occurred when querying models using paramters.

CausalQueries 1.1.0

CRAN release: 2024-04-10

Non Backwards Compatible Changes

Accessing causal-model objects via get_ methods e.g. get_nodal_types(), get_parameters is no longer supported. Objects may now be accessed via a unified syntax through the inspect() function (see New Functionality). The following functions are no longer exported:

• get_causal_types()
• get_nodal_types()
• get_all_data_types()
• get_event_probabilities()
• get_ambiguities_matrix()
• get_parameters()
• get_parameter_names()
• get_parmap()
• get_parameter_matrix()
• get_priors()
• get_param_dist()
• get_type_prob_multiple()

New Functionality

1. unified object access syntax via inspect()

causal-model objects can now be accessed via inspect() like so:

inspect(model, "parameters_df")

See documentation for an exhaustive list of accessible objects. causal-model objects now additionally come with dedicated print methods returning short informative summaries of the given object.

2. model diagnostics

A summary of parameter values and convergence information produced by the update_model() Stan model can now be accessed via:

inspect(model, "stan_summary")

Advanced model diagnostics on raw Stan output via external packages is possible by saving the stan_fit object when updating. This is facilitated via the keep_fit option in update_model():

model <- make_model("X -> Y") |>
  update_model(data, keep_fit = TRUE)

model |> inspect("stanfit")

CausalQueries 1.0.2

CRAN release: 2024-01-15

Bug Fixes

1. passing nodal_types to make_model() now implements correct error handling

Previously this make_model("X -> Y" , nodal_types = list(Y = c("0", "1"))) was permissible leading to setting nodal_types:

$X
NULL

$Y
[1] "0" "1"

This led to undefined behavior and unhelpful downstream error messages. When passing nodal_types to make_model() users are now forced to specify a set of nodal_types on each node.

2. query_distribution() are no longer overwrites type distribution internally

3. node naming checks are operational in make_model()

Previously hyphenated names would not throw an error and be corrupted silently through the conversion of model definition strings into dagitty objects.

make_model("institutions -> political-inequality")

Statement:
[1] "institutions -> political-inequality"

DAG:
        parent  children
1 institutions political

Checks for correct variable naming are now reinstated.

Improvements

1. type safety

Calls to sapply() have ben replaced with vapply() wherever possible to enforce type safety.

2. range based looping

Looping via index has been replaced by range based looping wherever possible to guard against 0 length exceptions.

3. goodpractice::gp()

goodpractice code improvements have been implemented.

CausalQueries 1.0.0

CRAN release: 2023-10-13

Non Backwards Compatible Changes

query_distribution() now supports the use of multiple queries in one function call and thus returns a DataFrame of distribution draws instead of a single numeric vector.

New Functionality

Querying

query_distribution(): now supports the specification of multiple queries and givens to be evaluated on a single model in one function call.

 model <- make_model("X -> Y")

 query_distribution(model,
   query = list("(Y[X=1] > Y[X=0])", "(Y[X=1] < Y[X=0])"),
   given = list("Y==1", "(Y[X=1] <= Y[X=0])"),
   using = "priors")|>
 head()

query_model(): now supports the specification of multiple models to evaluate a set of queries on in one function call.

 models <- list(
  M1 = make_model("X -> Y"),
  M2 = make_model("X -> Y") |> set_restrictions("Y[X=1] < Y[X=0]")
  )

 query_model(
  models,
  query = list(ATE = "Y[X=1] - Y[X=0]", Share_positive = "Y[X=1] > Y[X=0]"),
  given = c(TRUE,  "Y==1 & X==1"),
  using = c("parameters", "priors"),
  expand_grid = FALSE)

 query_model(
  models,
  query = list(ATE = "Y[X=1] - Y[X=0]", Share_positive = "Y[X=1] > Y[X=0]"),
  given = c(TRUE,  "Y==1 & X==1"),
  using = c("parameters", "priors"),
  expand_grid = TRUE)

This eliminates the need for redundant function calls when querying models and substantially improves computation time as computationally expensive function calls to produce data structures required for querying are now reduced to a minimum via redundancy elimination and caching.

Realising Outcomes and Interpreting Nodal-/Causal-Types

realise_outcomes(): specifying the node option now produces a DataFrame detailing how the specified node responds to its parents in the presence or absence of do operations. This produces a reduced form of the usual realise_outcomes() output detailing all causal-types; and aids in the interpretation of both nodal- and causal-types. This update resolves previous bugs and errors relating to specification of nodes with multiple parents in the node option.

 model <- make_model("X1 -> M -> Y -> Z; X2 -> Y") |>
  realise_outcomes(dos = list(M = 1), node = "Y") 

Bug Fixes

1. Setting Parameters and Priors

Previously set_parameters() and set_priors() would default applying changes in the order in which parameters appeared in the parameters_df DataFrame; regardless of the order in which changes were specified in the aforementioned functions. Calling:

 model <- make_model("X -> Y")
 set_priors(model, alphas = c(3,4), nodal_type = c("10",00))

would results in the following parameters_df.

  param_names node    gen param_set nodal_type given param_value priors
  <chr>       <chr> <int> <chr>     <chr>      <chr>       <dbl>  <dbl>
1 X.0         X         1 X         0          ""           0.5       1
2 X.1         X         1 X         1          ""           0.5       1
3 Y.00        Y         2 Y         00         ""           0.25      3
4 Y.10        Y         2 Y         10         ""           0.25      4
5 Y.01        Y         2 Y         01         ""           0.25      1
6 Y.11        Y         2 Y         11         ""           0.25      1

Now changes to parameters values get applied in the order specified in the function call; resulting in the following parameters_df for the above example:

  param_names node    gen param_set nodal_type given param_value priors
  <chr>       <chr> <int> <chr>     <chr>      <chr>       <dbl>  <dbl>
1 X.0         X         1 X         0          ""           0.5       1
2 X.1         X         1 X         1          ""           0.5       1
3 Y.00        Y         2 Y         00         ""           0.25      4
4 Y.10        Y         2 Y         10         ""           0.25      3
5 Y.01        Y         2 Y         01         ""           0.25      1
6 Y.11        Y         2 Y         11         ""           0.25      1

Additionally we have implemented helpful warnings for when instructions identifying parameters to be updated are under specified. This is particularly useful when setting priors or parameters on models with confounding as changes may inadvertently be applied across param_sets.

2. Updating with Censored Types

Previously updating models with censored types would fail as 0s in the w vector induced by censoring would evaluate to -Inf as the Stan MCMC algorithm began sampling from the posterior of the multinational distribution. We resolved this issue by pruning the w vector when the multinomial is run. This preserves the true w vector (event probabilities without censoring) while still updating with the censored data-

3. Setting Restrictions with Wild Cards

Previously wildcards in set_restrictions() were erroneously interpreted as valid nodal types, leading to errors and undefined behavior. Proper unpacking and mapping of wildcards to existing nodal types has been restored.

4. Checks for Misspecified Queries

Previously misspecifications in queries like Y[X==1]=1 would lead to undefined behavior when mapping queries to nodal or causal types. We now correct misspecified queries internally and warn about the misspecification. For example; running:

model <- CausalQueries::make_model("X -> Y")
get_query_types(model, "Y[X=1]=1")

now produces

Causal types satisfying query's condition(s)

 query =  Y[X=1]==1

X0.Y01  X1.Y01
X0.Y11  X1.Y11


 Number of causal types that meet condition(s) =  4
 Total number of causal types in model =  8
Warning message:
In check_query(query) :
  statements to the effect that the realization of a node should equal some value should be specified with `==` not `=`.
  The query has been changed accordingly: Y[X=1]==1

5. Allowing overwriting of a Parameter Matrix

Previously a parameter matrix P that was attached to a causal_model object could not be overwritten. Overwrites are now possible.

Improvements

1. Fast realise_outcomes()

We achieved a ~100 fold speed gain in the realise_outcomes() functionality. Nodal types on a given node are generated as the Cartesian product of parent realizations. Consider the meaning of nodal types on a node $Y$ with 3 parents $[X1,X2,X3]$:

X1	X2	X3
0	0	0
1	0	0
0	1	0
1	1	0
0	0	1
1	0	1
0	1	1
1	1	1

Each row in the above DataFrame corresponds to a digit in Y's nodal types. The first digit of each nodal type of $Y$ (see first row above), corresponds to the realization of $Y$ when $X1 = 0, X2 = 0, X3 = 0$. The fourth digit of each nodal type of $Y$ (see fourth row above), corresponds to the realization of $Y$ when $X1 = 1, X2 = 1, X3 = 0$. Finding the position of the realization value of $Y$ in a nodal type given parent realizations is equivalent to finding the row number in the Cartesian product DataFrame. By definition of the Cartesian product, the number of consecutive 0 or 1 elements in a given column is $2^{columnindex}$, when indexing columns from 0. Given a set of parent realizations $R$ indexed from 0, the corresponding row in a number in a DataFrame indexed from 0 can thus be computed via: $$row = (\sum_{i = 0}^{|R| - 1} (2^{i} \times R_i))$$. We implement a fast C++ version of this computing powers of 2 via bit shifting.

2. Stan update

We updated to the new array syntax introduced in Stan v2.33.0