Causal Inference for Qualitative Outcomes under Difference-in-Differences

causalQual_did(Y, D, unit_id, time)

Arguments

Y: Qualitative outcome before treatment. Must be labeled as $\{1, 2, \dots\}$.
D: Binary treatment indicator.
unit_id: Unit identifier.
time: Time identifier.
treatment_start: Denots at which time treatment starts. Must be zero for never-treated.
...: Other arguments for the att_gt function.

Value

An object of class causalQual.

Details

Under a difference-in-difference design, identification requires that the probabilities time shift for $Y_{is} (0)$ for class $m$ evolve similarly for the treated and control groups (parallel trends on the probability mass functions of $Y_{is}(0)$). If this assumption holds, we can recover the probability of shift on the treated for class $m$:

$$\delta_{m, T} := P(Y_{it} (1) = m | D_i = 1) - P(Y_{it}(0) = m | D_i = 1).$$

causalQual_did applies, for each class $m$, the canonical two-group/two-period method to the binary variable $1(Y_{is} = m)$. Specifically, consider the following linear model:

$$1(Y_{is} = m) = D_i \beta_{m1} + 1(s = t) \beta_{m2} + D_i 1(s = t) \beta_{m3} + \epsilon_{mis}.$$

The OLS estimate $\hat{\beta}_{m3}$ of $\beta_{m3}$ is our estimate of the probability shift on the treated for class m. Standard errors are clustered at the unit level and used to construct conventional confidence intervals.

References

Di Francesco, R., and Mellace, G. (2025). Causal Inference for Qualitative Outcomes. arXiv preprint arXiv:2502.11691. doi:10.48550/arXiv.2502.11691 .

Author

Riccardo Di Francesco

Examples

## Generate synthetic data.
set.seed(1986)

data <- generate_qualitative_data_did(100, assignment = "observational",
                                      outcome_type = "ordered")

Y <- data$Y
D <- data$D
unit_id <- data$unit_id
time <- data$time

## Estimate probabilities of shift on the treated.
fit <- causalQual_did(Y, D, unit_id, time)

summary(fit)
#> 
#> ── CAUSAL INFERENCE FOR QUALITATIVE OUTCOMES ───────────────────────────────────
#> 
#> ── Research design ──
#> 
#> Identification:          Difference-in-Differences 
#> Estimand:                Probability Shifts on the Treated 
#> Outcome type:            
#> Classes:                 1 2 3 
#> N. units:                100 
#> Fraction treated units:  0.48 
#> 
#> 
#> ── Point estimates and 95\% confidence intervals ──
#> 
#> Class 1: -0.543  [-0.760, -0.326]
#> Class 2: -0.120  [-0.343,  0.103]
#> Class 3:  0.663  [ 0.465,  0.862]
plot(fit)