Process tracing estimator — process_tracing

Answers a causal query about one case using a causal model from 'CausalQueries', once for each process tracing strategy. A strategy names the nodes whose values are observed, so the estimate shows how the answer changes as more of the case's data is examined.

Usage

process_tracing_estimator(causal_model, query, data, strategies)

Arguments

causal_model: A model from CausalQueries::make_model().
query: The causal query, in 'CausalQueries' syntax, e.g. "Y[X=1] > Y[X=0]".
data: A one-row data frame giving the case's value on each observed node. Nodes that are NA are treated as unobserved.
strategies: A character vector of strategies, each listing the nodes observed, separated by hyphens, e.g. c("X-Y", "X-M-Y").

Value

A data frame with one row per strategy: term (the strategy), XY (the case's values of X and Y, e.g. "X1Y1"), and estimate (the posterior mean of the query).

Details

See https://book.declaredesign.org/observational-causal.html#process-tracing

Examples

# Simple example showing ambiguity in attribution
process_tracing_estimator(
  causal_model = CausalQueries::make_model("X -> Y"),
  query = "Y[X=1] > Y[X=0]",
  data = data.frame(X=1, Y = 1),
  strategies = "X-Y")
#> 
#> Causal queries generated by query_model
#> 
#> |term |XY   | estimate|
#> |:----|:----|--------:|
#> |X-Y  |X1Y1 |      0.5|
#> 

# Example where M=1 acts as a hoop test
process_tracing_estimator(
  causal_model = CausalQueries::make_model("X -> M -> Y") |>
   CausalQueries::set_restrictions("Y[M=1] < Y[M=0]") |>
   CausalQueries::set_restrictions("M[X=1] < M[X=0]"),
  query = "Y[X=1] > Y[X=0]",
  data = data.frame(X=1, Y = 1, M = 0),
  strategies = c("Y", "X-Y", "X-M-Y"))
#> 
#> Causal queries generated by query_model
#> 
#> |term  |XY   | estimate|
#> |:-----|:----|--------:|
#> |Y     |X1Y1 |    0.111|
#> |X-Y   |X1Y1 |    0.200|
#> |X-M-Y |X1Y1 |    0.000|
#>