Use this Skill when the user needs to estimate causal treatment effects using difference-in-differences (DID) designs: two-way fixed effects (TWFE) regression, parallel trends pre-testing, Callaway-Sant'Anna staggered adoption estimator, and Goodman-Bacon decomposition. Covers both Python (linearmodels) and R (did package).
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Use this Skill when the user needs to estimate causal treatment effects using difference-in-differences (DID) designs: two-way fixed effects (TWFE) regression, parallel trends pre-testing, Callaway-Sant'Anna staggered adoption estimator, and Goodman-Bacon decomposition. Covers both Python (linearmodels) and R (did package).
{"python":["linearmodels>=4.28","pandas>=1.5.0","numpy>=1.23.0","matplotlib>=3.6.0","statsmodels>=0.14.0"],"r":["did (Callaway-Sant'Anna)","bacondecomp (Goodman-Bacon)","fixest (fast TWFE with multiple FE)"]}
last_updated
2026-03-17
Difference-in-Differences Causal Inference
TL;DR — Estimate causal treatment effects with DID designs: classic 2x2 DID,
Two-Way Fixed Effects (TWFE), parallel trends testing, staggered adoption with
Callaway-Sant'Anna, and Goodman-Bacon decomposition.
1. Overview
What Problem Does This Skill Solve?
Difference-in-differences is the workhorse of applied microeconometrics. It identifies
causal effects by comparing treated and control units before and after treatment,
under the assumption that untreated units provide a valid counterfactual trend.
Recent methodological advances (2018–2023) show that the canonical TWFE estimator
is biased under staggered adoption when treatment effects are heterogeneous across
cohorts. This Skill implements the full modern DID toolkit:
Method
When to Use
Classic 2x2 DID
Single treatment date, clear control group
TWFE with unit + time FE
Panel data, homogeneous effects assumed
Parallel trends pre-test
Always — validates identifying assumption
Callaway-Sant'Anna (C&S)
Staggered rollout, heterogeneous effects
Goodman-Bacon decomposition
Diagnose TWFE bias from staggered adoption
Key Identifying Assumption
Parallel trends: In the absence of treatment, the average outcomes of treated and
control units would have followed parallel paths over time. This is untestable
for the post-period but can be assessed using pre-treatment periods as placebo tests.
2. Environment Setup
# Python environment
conda create -n did python=3.11 -y
conda activate did
pip install linearmodels pandas numpy matplotlib statsmodels
# R packages (run in R console)# install.packages(c("did", "bacondecomp", "fixest", "dplyr", "ggplot2"))
Verify Python setup:
import linearmodels
import pandas as pd
import numpy as np
print(f"linearmodels version: {linearmodels.__version__}")
3. Core Implementation
3.1 Generate Synthetic Panel Data
import numpy as np
import pandas as pd
np.random.seed(42)
defgenerate_did_panel(
n_units: int = 200,
n_periods: int = 10,
treat_fraction: float = 0.5,
treatment_period: int = 6,
true_att: float = 2.0,
parallel_trends_violation: float = 0.0,
) -> pd.DataFrame:
"""
Generate a synthetic balanced panel for DID exercises.
Args:
n_units: Number of units (firms / individuals / regions).
n_periods: Number of time periods (e.g. years).
treat_fraction: Fraction of units assigned to treatment.
treatment_period: Period when treatment begins (1-indexed).
true_att: True average treatment effect on the treated.
parallel_trends_violation: Non-zero values introduce a pre-trend for testing.
Returns:
DataFrame with columns: unit_id, period, treated, post, y, x1, x2.
"""
unit_ids = np.arange(1, n_units + 1)
treated_units = np.random.choice(unit_ids, size=int(n_units * treat_fraction), replace=False)
treated_set = set(treated_units)
unit_fe = np.random.normal(0, 1, n_units)
period_fe = np.linspace(0, 2, n_periods)
rows = []
for i, uid inenumerate(unit_ids):
is_treated = uid in treated_set
for t inrange(1, n_periods + 1):
post = int(t >= treatment_period)
treatment_indicator = is_treated and post
pre_trend = parallel_trends_violation * is_treated * (t - treatment_period) * (1 - post)
y = (
unit_fe[i]
+ period_fe[t - 1]
+ true_att * treatment_indicator
+ pre_trend
+ np.random.normal(0, 0.5)
)
rows.append({
"unit_id": uid,
"period": t,
"treated": int(is_treated),
"post": post,
"did": int(is_treated and post),
"y": y,
"x1": np.random.normal(0, 1),
"x2": np.random.binomial(1, 0.4),
})
df = pd.DataFrame(rows)
df = df.set_index(["unit_id", "period"])
return df
3.2 Two-Way Fixed Effects (TWFE)
from linearmodels import PanelOLS
import warnings
defrun_twfe_did(
df: pd.DataFrame,
outcome: str = "y",
treatment: str = "did",
covariates: list = None,
cluster_by: str = "unit_id",
) -> dict:
"""
Estimate a TWFE DID model: y_it = alpha_i + lambda_t + beta*D_it + X_it*gamma + e_it.
Args:
df: Panel DataFrame with MultiIndex (unit_id, period).
outcome: Name of the outcome variable column.
treatment: Name of the treatment indicator column (1 if treated and post).
covariates: List of additional control variable names.
cluster_by: Level of clustering for standard errors ('unit_id' recommended).
Returns:
Dictionary with keys: coef, se, tstat, pvalue, ci_low, ci_high, n_obs, r2.
"""
covariates = covariates or []
regressors = [treatment] + covariates
formula = f"{outcome} ~ {' + '.join(regressors)} + EntityEffects + TimeEffects"with warnings.catch_warnings():
warnings.simplefilter("ignore")
model = PanelOLS.from_formula(formula, data=df)
result = model.fit(cov_type="clustered", cluster_entity=True)
params = result.params
tstat = result.tstats
pval = result.pvalues
ci = result.conf_int()
return {
"coef": float(params[treatment]),
"se": float(result.std_errors[treatment]),
"tstat": float(tstat[treatment]),
"pvalue": float(pval[treatment]),
"ci_low": float(ci.loc[treatment, "lower"]),
"ci_high": float(ci.loc[treatment, "upper"]),
"n_obs": result.nobs,
"r2": result.rsquared,
"summary": result.summary,
}
3.3 Parallel Trends Pre-Test
import matplotlib.pyplot as plt
deftest_parallel_trends(
df: pd.DataFrame,
outcome: str = "y",
treatment_period: int = 6,
output_path: str = None,
) -> pd.DataFrame:
"""
Event-study plot for parallel trends pre-test.
For each period t, estimate the interaction (treated_i x 1[period=t]) to
check whether treated and control units diverge before treatment. All
coefficients in pre-periods should be statistically indistinguishable from 0.
Args:
df: Panel DataFrame with MultiIndex (unit_id, period).
outcome: Outcome variable name.
treatment_period: The first treated period (base period will be t-1).
output_path: If given, save the event-study plot here.
Returns:
DataFrame of event-study coefficients: period, coef, ci_low, ci_high.
"""
df_reset = df.reset_index()
periods = sorted(df_reset["period"].unique())
# Create period dummies interacted with treated
base_period = treatment_period - 1
event_dummies = []
for t in periods:
if t == base_period:
continue
col = f"event_{t}"
df_reset[col] = (df_reset["treated"] == 1) & (df_reset["period"] == t)
df_reset[col] = df_reset[col].astype(int)
event_dummies.append(col)
df_indexed = df_reset.set_index(["unit_id", "period"])
formula = f"{outcome} ~ {' + '.join(event_dummies)} + EntityEffects + TimeEffects"with warnings.catch_warnings():
warnings.simplefilter("ignore")
model = PanelOLS.from_formula(formula, data=df_indexed)
result = model.fit(cov_type="clustered", cluster_entity=True)
ci = result.conf_int()
records = []
for col in event_dummies:
t = int(col.replace("event_", ""))
records.append({
"period": t,
"coef": float(result.params[col]),
"ci_low": float(ci.loc[col, "lower"]),
"ci_high": float(ci.loc[col, "upper"]),
"relative_period": t - treatment_period,
})
# Add base period at 0
records.append({
"period": base_period,
"coef": 0.0,
"ci_low": 0.0,
"ci_high": 0.0,
"relative_period": -1,
})
es_df = pd.DataFrame(records).sort_values("period").reset_index(drop=True)
# Plot
fig, ax = plt.subplots(figsize=(9, 4))
ax.axhline(0, color="black", linewidth=0.8, linestyle="--")
ax.axvline(-0.5, color="red", linewidth=1, linestyle=":", label="Treatment onset")
ax.errorbar(
es_df["relative_period"],
es_df["coef"],
yerr=[es_df["coef"] - es_df["ci_low"], es_df["ci_high"] - es_df["coef"]],
fmt="o",
capsize=4,
color="#4C72B0",
)
ax.set_xlabel("Periods relative to treatment")
ax.set_ylabel(f"Coef on {outcome}")
ax.set_title("Event Study — Parallel Trends Pre-Test")
ax.legend()
fig.tight_layout()
if output_path:
fig.savefig(output_path, dpi=150)
print(f"Saved event-study plot to {output_path}")
return es_df
3.4 Placebo Check
defrun_placebo_check(
df: pd.DataFrame,
outcome: str = "y",
true_treatment_period: int = 6,
n_placebo_periods: int = 3,
) -> pd.DataFrame:
"""
Run placebo DID regressions using pre-treatment periods as fake treatment dates.
All placebo estimates should be statistically insignificant (p > 0.05).
A significant placebo suggests violations of parallel trends.
Args:
df: Panel DataFrame with MultiIndex (unit_id, period).
outcome: Outcome variable column name.
true_treatment_period: Actual treatment start period.
n_placebo_periods: Number of fake treatment periods to test.
Returns:
DataFrame of placebo estimates: placebo_period, coef, pvalue, significant.
"""
df_reset = df.reset_index()
pre_periods = sorted(
[p for p in df_reset["period"].unique() if p < true_treatment_period - 1]
)
placebo_periods = pre_periods[-n_placebo_periods:]
results = []
for fake_period in placebo_periods:
df_placebo = df_reset[df_reset["period"] < true_treatment_period].copy()
df_placebo["did_placebo"] = (
(df_placebo["treated"] == 1) & (df_placebo["period"] >= fake_period)
).astype(int)
df_indexed = df_placebo.set_index(["unit_id", "period"])
formula = f"{outcome} ~ did_placebo + EntityEffects + TimeEffects"with warnings.catch_warnings():
warnings.simplefilter("ignore")
model = PanelOLS.from_formula(formula, data=df_indexed)
res = model.fit(cov_type="clustered", cluster_entity=True)
results.append({
"placebo_period": fake_period,
"coef": float(res.params["did_placebo"]),
"pvalue": float(res.pvalues["did_placebo"]),
"significant": float(res.pvalues["did_placebo"]) < 0.05,
})
return pd.DataFrame(results)
3.5 Callaway-Sant'Anna in R (Staggered Adoption)
For staggered treatment rollout, TWFE is biased. Use the Callaway-Sant'Anna estimator
via the R did package.
# Install once: install.packages(c("did", "dplyr", "ggplot2"))
library(did)
library(dplyr)
library(ggplot2)# ── Data preparation ────────────────────────────────────────────────────# Required columns:# - unit_id : unit identifier (integer)# - period : time period (integer)# - y : outcome variable# - g : first treatment period (0 = never treated)
df <- read.csv("panel_staggered.csv")# Units that are never treated must have g = 0
df <- df %>%
mutate(g = ifelse(is.na(first_treat_period),0, first_treat_period))# ── Callaway-Sant'Anna ATT(g,t) ─────────────────────────────────────────
cs_result <- att_gt(
yname ="y",
tname ="period",
idname ="unit_id",
gname ="g",
xformla =~ x1 + x2,# optional covariates
data = df,
control_group ="nevertreated",# "notyettreated" is also valid
est_method ="reg",# regression adjustment
anticipation =0,# periods of anticipation
clustervars ="unit_id",)
summary(cs_result)# ── Aggregate to overall ATT ─────────────────────────────────────────────
att_overall <- aggte(cs_result, type ="simple")
summary(att_overall)# ── Event-study aggregation ─────────────────────────────────────────────
att_event <- aggte(cs_result, type ="dynamic", min_e =-4, max_e =4)
summary(att_event)# ── Plot event study ────────────────────────────────────────────────────
ggdid(att_event)+
labs(
title ="Callaway-Sant'Anna Event Study",
subtitle = paste0("Overall ATT = ",round(att_overall$overall.att,3)," (p = ",round(att_overall$overall.se,3),")"))
3.6 Goodman-Bacon Decomposition in R
# Diagnose TWFE bias from heterogeneous staggered treatment effects
library(bacondecomp)
bacon_result <- bacon(
formula = y ~ treated_post,
data = df,
id_var ="unit_id",
time_var ="period",)# Print the decomposition table
print(bacon_result)# Visualize: scatter plot of 2x2 DID weights vs estimates
bacon_df <- as.data.frame(bacon_result)
ggplot(bacon_df, aes(x = weight, y = estimate, color = type))+
geom_point(size =2, alpha =0.8)+
geom_hline(yintercept =0, linetype ="dashed")+
labs(
title ="Goodman-Bacon Decomposition",
x ="2x2 DID Weight",
y ="2x2 DID Estimate",
color ="Comparison type",)+
theme_minimal()
# Generate staggered treatment panel
np.random.seed(1)
n_units = 400
periods = np.arange(1, 13)
treat_cohorts = {0: None, 4: "early", 7: "mid", 10: "late"} # 0 = never treated
rows = []
for uid inrange(1, n_units + 1):
cohort_key = np.random.choice(list(treat_cohorts.keys()), p=[0.25, 0.25, 0.25, 0.25])
first_treat = cohort_key if cohort_key > 0else999for t in periods:
post = int(t >= first_treat)
# Heterogeneous ATT: early cohort gets larger effect
att = 3.0if cohort_key == 4else1.5if cohort_key == 7else0.8
y = uid * 0.01 + t * 0.5 + att * post + np.random.normal(0, 0.5)
rows.append({"unit_id": uid, "period": t, "y": y,
"treated_post": post, "g": cohort_key if cohort_key > 0else0})
df_stag = pd.DataFrame(rows).set_index(["unit_id", "period"])
# TWFE on staggered data -- will be biased!
twfe_stag = run_twfe_did(df_stag, outcome="y", treatment="treated_post")
print(f"TWFE on staggered data: {twfe_stag['coef']:.3f} (biased -- use C&S instead!)")
# Correct approach: use Callaway-Sant'Anna in R (see Section 3.5 above)print("Export to CSV and run the R code in Section 3.5 for unbiased estimates.")
df_stag.reset_index().to_csv("panel_staggered.csv", index=False)
5. Common Errors and Troubleshooting
Error
Cause
Fix
Singleton groups found in the data
Unit with only 1 observation
Drop singletons or use drop_absorbed=True in fixest
ValueError: Panel is not balanced
Missing unit-period observations
Impute or use unbalanced panel estimator
TWFE ATT far from truth on staggered data
Heterogeneous treatment effects
Switch to Callaway-Sant'Anna or stacked DID
Significant pre-trends in event study
Parallel trends violation
Check for confounders; consider synthetic control
R att_gt() returns NA for some cohorts
Too few never-treated units
Use control_group="notyettreated"
CollinearityWarning in linearmodels
Redundant fixed effects or perfect multicollinearity
Drop redundant columns; check for time-invariant regressors
6. Methodological Notes
Why TWFE fails under staggered adoption: In a staggered design, early-treated units
act as controls for later-treated units. If treatment effects grow over time (dynamic
effects), already-treated units provide a bad counterfactual, creating negative weights
in the TWFE decomposition and biasing the aggregate estimate.
Callaway-Sant'Anna solution: Estimates ATT(g,t) — the average treatment effect for
cohort g at time t — using only clean comparisons (never-treated or not-yet-treated
units as controls). Aggregates to overall ATT or event-study coefficients without
imposing effect homogeneity.
Rule of thumb: Use TWFE when treatment timing is uniform. Use C&S whenever there
is staggered rollout and you suspect heterogeneous effects.
7. References and Further Reading
Callaway, B. and Sant'Anna, P. (2021). "Difference-in-Differences with Multiple Time Periods."
Journal of Econometrics, 225(2), 200–230. https://doi.org/10.1016/j.jeconom.2020.12.001
