# Chapter 12 Notation and syntax

## 12.1 Notation

A guide to key notation used in the CausalQueries package:

term symbol meaning
nodal type $$\theta^X$$ The way that a node responds to the values of its parents. Example: $$\theta^Y_{10}$$, written Y10: $$Y$$ takes the value 1 if $$X=0$$ and 0 if $$X=1$$. For interpretation of syntax, see make_model("X->Y") %>% get_nodal_types
causal type $$\theta$$ A causal type is a concatenation of nodal types, one for each node. Example: $$(\theta^X_0, \theta^Y_{00})$$, written X0.Y00, is a type that has $$X=0$$ and $$Y=0$$ no matter what the value of $$X$$. See: make_model("X->Y") %>% get_causal_types
parameter $$\lambda$$ An unknown quantity of interest that generates the probability of causal types. In models without confounding parameters are the probabilities of nodal types. In models with confounding, parameters are the conditional probabilities of causal types. Example: X.0$$=\lambda^X_0 = \Pr(\theta^X = \theta^X_0)$$ See make_model("X->Y") %>% get_parameters
data event type A possible set of values on all nodes (including, possibly, NAs). Example: X0Y1 $$= (X=0, Y = 1)$$
event probability $$w$$ The probability of a data event type. Example: $$w_{00}=\Pr(X=0, Y=1)$$
Dirichlet priors alpha, $$\alpha$$ Non negative numbers used to characterize a prior distribution over a simplex. The implied mean is the normalized vector $$\mu= \alpha/\sum(\alpha)$$ and the variance is $$\mu(1-\mu)/(1+\sum\alpha)$$.
parameter matrix $$P$$ A matrix of 0s and 1s that maps from parameters (rows) to causal types (columns). Example: see make_model("X->Y") %>% get_parameter_matrix
ambiguities matrix $$A$$ A matrix of 0s and 1s that maps from causal types (rows) to data types (columns). Example: see make_model("X->Y") %>% get_ambiguities_matrix
families matrix $$E$$ A matrix of 0s and 1s that maps from partial data events to complete data events. Example: see make_model("X->Y") %>% get_data_families
data strategy $$S$$ A plan indicating for how many nodes different types of data will be gathered. See ? make_data

### 12.1.1 Parents, children, and all that

The causal models analyzed by CausalQueries all involve directed edges between nodes, with cycles over nodes precluded. In turn this implies a partial ordering over nodes which motivates some useful terminology:

• $$X$$ is a parent of $$Y$$ if a change in $$X$$ sometimes induces a change in $$Y$$ even when all other nodes are fixed. On the graph, there’s an arrow from $$X$$ to $$Y$$.
• $$Y$$ is a child of $$X$$ if a change in $$X$$ sometimes induces a change in $$Y$$ even when all other nodes are fixed. On the graph, there’s an arrow from $$X$$ to $$Y$$.
• $$A$$ is an ancestor of $$B$$ by analogy: a parent is an ancestor and any parent of an ancestor is an ancestor. On the graph there is a chain of arrows pointing in one direction going from $$A$$ to $$B$$. Similarly for descendant.

You should find that the package complains if you try to specify a cyclical graph.

## 12.2 Causal syntax

Both model definition and model querying requires a simple way to make arbitrary causal statements.

• You can query observational quantities. For instance:
• make_model("X->Y") %>% get_query_types("Y==1") Figures out the types that produce $$Y=1$$ absent any interventions.
• You can query experimental quantities. For instance:
• make_model("X->Y") %>% get_query_types("Y[X=1]==1") figures out the types that produce $$Y=1$$ when $$X$$ is set to 1.
• make_model("X->M->Y") %>% get_query_types("Y[X=1]>Y[X=0]") figures out the types that have a positive causal effect.
• You can make queries with complex counterfactuals. For instance:
• make_model("X->M->Y") %>% get_query_types("Y[M=M[X=0], X=1]==1") looks for the types for which $$Y=1$$ when $$X=1$$ and $$M$$ is held constant at the value it would take if $$X$$ were 0.
• You can use wild cards and AND or OR operators. For instance:
• make_model("X->Y") %>% get_query_types("(Y[X = .]==1)", join_by = "|") figures out the causal types for which $$Y=1$$ for some value of $$X$$.
• make_model("X->Y") %>% get_query_types("(Y[X = .]==1)", join_by = "&") figures out the causal types for which $$Y=1$$ for all values of $$X$$.
• Note that the use of “.” as a wild card also requires placing the causal statement in parentheses, as in these examples.
• You can make conditional queries. For instance, conditioning on observational or counterfactual quantities:
• make_model("X->Y") %>% query_model("Y[X = 1] > Y[X = 0]", subset = "X==1 & Y==1") asks what is the probability that $$X$$ has a positive effect on $$Y$$ given $$X=Y=1$$.
• make_model("X->M->Y") %>% query_model("Y[X = 1] != Y[X = 0]", subsets = "M[X=1]==M[X=0]") asks what is the probability that $$X$$ matters for $$Y$$ given $$X$$ doesn’t matter for $$M$$.

We provide a few helpers for common causal statements:

• increasing("A", "B") produces the statement "B[A=1] > B[A=0]"
• decreasing("A", "B") produces the statement "B[A=1] < B[A=0]"
• interacts("A", "B", "C") produces the statement "((C[A =1, B = 1]) - (C[A = 0, B = 1])) != ((C[A =1, B = 0]) - (C[A = 0, B = 0]))"
• complements("A", "B", "C") produces the statement "((C[A =1, B = 1]) - (C[A = 0, B = 1])) > ((C[A =1, B = 0]) - (C[A = 0, B = 0]))"
• substitutes("A", "B", "C") produces the statement "((C[A =1, B = 1]) - (C[A = 0, B = 1])) < ((C[A =1, B = 0]) - (C[A = 0, B = 0]))"

These helpers can be used for setting restrictions, setting confounds, defining priors or parameter values, or querying models.

For instance:

get_query_types(model = make_model("A -> B <- C"),
query = substitutes("A", "C", "B"))

Causal types satisfying query's condition(s)

query =  ((B[A = 1, C = 1]) - (B[A = 0, C = 1])) < ((B[A = 1, C = 0]) - (B[A = 0, C = 0]))

A0.C0.B0100  A1.C0.B0100
A0.C1.B0100  A1.C1.B0100
A0.C0.B0010  A1.C0.B0010
A0.C1.B0010  A1.C1.B0010
A0.C0.B0110  A1.C0.B0110
A0.C1.B0110  A1.C1.B0110
A0.C0.B1110  A1.C0.B1110
A0.C1.B1110  A1.C1.B1110
A0.C0.B0111  A1.C0.B0111
A0.C1.B0111  A1.C1.B0111

Number of causal types that meet condition(s) =  20
Total number of causal types in model =  64