AMCEs from a conjoint administration

Average marginal component effects from a conjoint_instrument() administration: one OLS regression of profile choice on treatment-coded dummies for all attributes simultaneously, with CR1 cluster-robust standard errors clustered by persona and 95% intervals on the t distribution with G - 1 degrees of freedom (G personas). Under uniform, independent profile randomization this is the standard AMCE estimator.

Usage

amce(responses)

Arguments

responses

A panel_administer() result whose instrument came from conjoint_instrument().

Value

A tibble: attribute, level, estimate, std_error, ci_lo, ci_hi. Baseline levels (the first level present, in the design's order) appear with estimate 0 and std_error = NA, so the table feeds the familiar conjoint plot directly. Attributes n_profiles, n_respondents, and n_dropped_na record the profile rows used, the respondents administered, and missing task responses dropped.

References

Hainmueller, Jens, Daniel J. Hopkins, and Teppei Yamamoto (2014). "Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments." Political Analysis 22(1), 1-30.

Examples

if (FALSE) { # \dontrun{
set.seed(110)
panel <- panel_from_margins(list(group = c(A = .5, B = .5)), n = 6)
design <- conjoint_design(
  list(color = c("blue", "red"), cost = c("low", "high")),
  n_tasks = 6)
instr <- conjoint_instrument(design)
cfg <- LLMR::llm_config("groq", "openai/gpt-oss-20b")
r <- panel_administer(panel, instr, cfg)
amce(r)
} # }