This is a patch release fixing a bug in the `print.model_query()`

S3 method that occurred when querying models using `paramters`

.

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 `grab()`

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()`

`grab()`

`causal-model`

objects can now be accessed via `grab()`

like so:

`grab(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.

A summary of parameter values and convergence information produced by the `update_model()`

`Stan`

model can now be accessed via:

`grab(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 |> grab("stan_fit")
```

`nodal_types`

to `make_model()`

now implements correct error handlingPreviously 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.

`query_distribution()`

are no longer overwrites type distribution internally`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.

Calls to `sapply()`

have ben replaced with `vapply()`

wherever possible to enforce type safety.

Looping via index has been replaced by range based looping wherever possible to guard against 0 length exceptions.

`goodpractice::gp()`

`goodpractice`

code improvements have been implemented.

`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.

`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.

`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")
```

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`

.

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-

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.

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
```

Previously a parameter matrix `P`

that was attached to a `causal_model`

object could not be overwritten. Overwrites are now possible.

`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.

`Stan`

updateWe updated to the new array syntax introduced in `Stan`

`v2.33.0`