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Scenario2 min read

Difference in difference

We call 'Uncofoundedness' a scenario where a treatment is not randomly assigned to participants, so confounders effect on treatment assignment and outcome. We h...

Difference in difference

We call 'Uncofoundedness' a scenario where a treatment is not randomly assigned to participants, so confounders effect on treatment assignment and outcome. We have client - level data. Confounders were measured before treatment and outcome after

Result
unit_idcalendar_timetreated_timeyregionmarket_trafficavg_order_valuemarket_competitionmacro_indexseasonality_indexy_cfmu_cfmu_treatedtau_mean_truetau_realized_truetau_rate_true
0control_market_0012021-0103302.404671central2.27032243.6534590.4432700.9182981.0800003302.4046713312.5197313312.5197310.00.00.0
1control_market_0012021-0203464.267194central2.24636347.0896700.4536950.9513171.1200003464.2671943317.0064253317.0064250.00.00.0
2control_market_0012021-0304912.564041central2.44075245.5443940.4319900.9017931.0985644912.5640413929.7543433929.7543430.00.00.0
3control_market_0012021-0404145.708011central2.69408445.4365220.4024911.0787461.0800004145.7080114043.0132374043.0132370.00.00.0
4control_market_0012021-0503723.949154central2.49622044.2897410.4439950.9553921.0985643723.9491543438.5193753438.5193750.00.00.0
5control_market_0012021-0603405.257515central2.50441945.5355140.4470760.9296681.1200003405.2575153581.2966893581.2966890.00.00.0
6control_market_0012021-0703663.077982central2.41659645.3042310.4328380.9186531.0800003663.0779823304.6102623304.6102620.00.00.0
7control_market_0012021-0802636.664688central2.01274146.6679270.4416440.8931380.9600002636.6646882554.0590952554.0590950.00.00.0
8control_market_0012021-0902364.397235central1.85648745.4598950.4149381.0169150.8214362364.3972352102.8405092102.8405090.00.00.0
9control_market_0012021-1002537.998976central1.98897343.5337680.4014451.1373150.7600002537.9989762148.9051422148.9051420.00.00.0

Inference

Result
value
field
estimandaverage_post_effect
modelCallawaySantAnnaStaggeredDID
estimatordr
control_groupnot_yet_or_never
anticipation0
base_perioduniversal
include_pre_periodsFalse
value194.0136 (ci_abs: 112.9152, 275.1120)
std_error41.3775
alpha0.0500
p_value0.0000
is_significantTrue
n_att_gt_cells18
n_skipped_cells0
inferenceclustered_influence
time2026-05-17

Oracle effect

Result

np.float64(127.67201792553853)