CausalQueries 1.0.2

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Looping via index has been replaced by range based looping wherever possible to guard against 0 length exceptions.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

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