mdid: Mean Difference-in-Differences

In qte: Quantile Treatment Effects

Mean Difference-in-Differences

Description

Computes Quantile Treatment effects on the Treated (QTT) and the Average Treatment Effect on the Treated (ATT) using the Mean Difference-in-Differences identification strategy. Handles two-period data and staggered treatment adoption uniformly. Supports both panel and repeated cross sections data.

Identification. MDiD assumes the counterfactual distribution of untreated potential outcomes is a location shift of the treated group's pre-treatment distribution. The size of the shift is the mean DiD \Delta = E[Y_{\text{post}}|D=0] - E[Y_{\text{pre}}|D=0], so Q_{Y(0),\text{post}|D=1}(\tau) = Q_{Y,\text{pre}|D=1}(\tau) + \Delta. This is stronger than parallel trends in means alone: it additionally requires that the shape of the treated group's outcome distribution is unchanged in the counterfactual. MDiD is a special case of QDiD that applies a single mean shift rather than a rank-specific distributional shift.

Covariate adjustment. When xformula is specified, the scalar shift is replaced by a unit-specific conditional mean shift \Delta(X_i) = E[Y_{\text{post}}|D=0,X_i] - E[Y_{\text{pre}}|D=0,X_i], estimated by weighted OLS. The counterfactual for treated unit i is Y_{\text{pre},i} + \Delta(X_i), and the unconditional counterfactual distribution is the empirical CDF of these values. By the law of iterated expectations, this consistently estimates F_{Y(0),\text{post}|D=1}.

Usage

mdid(
  yname,
  gname,
  tname,
  idname = NULL,
  data,
  panel = TRUE,
  xformula = ~1,
  weightsname = NULL,
  control_group = "notyettreated",
  anticipation = 0,
  alp = 0.05,
  cband = TRUE,
  biters = 100,
  cl = 1,
  ret_quantile = NULL,
  gt_type = "att",
  probs = NULL
)

Arguments

yname	Name of the outcome variable in data.
gname	Name of the treatment group variable (first treatment period; 0 for never-treated units).
tname	Name of the time period variable.
idname	Name of the unit id variable. Required when panel = TRUE.
data	A data frame.
panel	Logical; TRUE (default) for panel data, FALSE for repeated cross sections.
xformula	One-sided formula for covariates. Default ~1 uses no covariates.
weightsname	Name of the column in data containing sampling weights. Default NULL uses equal weights.
control_group	Which units to use as the comparison group: "notyettreated" (default) or "nevertreated".
anticipation	Number of periods of anticipation. Default 0.
alp	Significance level for confidence bands. Default 0.05.
cband	Logical; if TRUE (default) compute a uniform confidence band rather than pointwise intervals.
biters	Number of bootstrap iterations. Default 100.
cl	Number of clusters for parallel computation. Default 1.
ret_quantile	Passed through to ptetools for the "qott" case.
gt_type	Type of group-time effect to compute. "att" (default) returns ATT(g,t). "qtt" returns the full QTT curve over probs using mixture-CDF aggregation. "qott" returns the quantile of the individual treatment effect distribution (panel only).
probs	For gt_type = "qtt", the quantile grid at which to evaluate the QTT curve. Default is seq(0.05, 0.95, 0.05).

Value

For gt_type = "att", a pte_results object from ptetools. For gt_type = "qtt", a pte_qtt object with overall, group-specific, and dynamic QTT curves and bootstrap SEs.

References

Athey, Susan and Guido Imbens. “Identification and Inference in Nonlinear Difference-in-Differences Models.” Econometrica 74(2), pp. 431-497, 2006.

Thuysbaert, Bram. “Distributional Comparisons in Difference in Differences Models.” Working Paper, 2007.

Examples

data(mpdta, package = "did")

## ATT aggregated across all groups and periods
res_att <- mdid(yname = "lemp", gname = "first.treat", tname = "year",
                idname = "countyreal", data = mpdta,
                gt_type = "att", biters = 20)
summary(res_att)

## Full QTT curve at selected quantiles
res_qtt <- mdid(yname = "lemp", gname = "first.treat", tname = "year",
                idname = "countyreal", data = mpdta,
                gt_type = "qtt", probs = seq(0.1, 0.9, 0.1), biters = 20)
summary(res_qtt)

qte documentation built on July 23, 2026, 5:13 p.m.