Changelog
Source:NEWS.md
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.
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.
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
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 2columnindex, 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.