Exploring the Spatial Impact of Multisource Data on Urban Vitality: A Causal Machine Learning Method

Table 5

Urban multisource data robustness test for causal inference for each data ( effect).

Data refute	Random vs new random	Unobserved vs new unobserved	Placebo vs new placebo
Human	0.00239 vs 0.00144	0.00239 vs 0.001315	0.00239 vs 0.000488
Factory	0.0597 vs 0.0588	0.0597 vs 0.0600	0.0597 vs 0.000823
School	0.465 vs 0.444	0.465 vs 0.482	0.465 vs −0.129
Office	0.168 vs 0.175	0.168 vs 0.178	0.168 vs −0.000200
Shop	0.326 vs 0.311	0.326 vs 0.300	0.326 vs 0.0222
Hospital	1.700 vs 1.380	1.700 vs 2.00	1.700 vs −0.340
Hotel	1.0373 vs 0.980	1.0373 vs 0.966	1.0373 vs −0.0881
Theater	−4.0220 vs −11.437	−4.0220 vs −11.722	−4.0220 vs −1.40567
Park	0.358 vs 0.387	0.358 vs 0.358	0.358 vs −0.00456
Restaurant	2090.452 vs 1991.236	2090.452 vs 149.234	2090.452 vs 102.148
Edu	1944.911 vs 1780.390	1944.911 vs 154.735	1944.911 vs 78.394
Health care	2144.268 vs 2060.782	2144.268 vs 176.677	2144.268 vs −317.822
Life	1461.368 vs 1520.191	1461.368 vs 230.915	1461.368 vs −100.435
Scenery	1980.187 vs 2093.490	1980.187 vs −36.0728	1980.187 vs −131.854
Transport	249.075 vs 366.321	249.075 vs 83.696	249.075 vs −24.373