Saltwater disposal (SWDs) has been linked to the recent increase of earthquakes in various regions of the United States. In some cases, the strong temporal and spatial associations have provided unequivocal evidences to the scientific community that wastewater injection is one of the dominant causal factors to the onset seismicity. In addition, numerous physical models have suggested that the increase in pore pressure from wastewater injection is capable to induce fault slips, providing further physical evidences. Another growing body of literature sorts to rigorously prove causality with statistical analysis where they propose statistical frameworks with parametric regression models to evaluate whether the observed earthquakes were occurring more often than by random chances and tested the statistical significance of the observed occurrences of earthquake to arrive at causal interpretations. We propose causal inference frameworks with the potential outcomes perspective to explicitly define what we meant by causal effect with mathematical formulations and declare necessary assumptions to ensure consistency between models for model comparison. In particular, we put considerations on two common difficulties in raster-based spatial statistical analysis, the spatial correlation, which can be described by Tobler’s first law of geography where near things are more related than distant things, and interference, a causal inference term, where treatments applied to some spatially indexed units affect the outcomes at other spatially indexed units, mostly due to complex physical processes. The study region, the Fort-Worth Basin of North Central Texas, is discretized into non-overlapping grid blocks. The first proposed workflow adopts a cross-sectional study design on aggregated earthquake catalog and injection data where two statistical methods are employed to test the significance of the causal effect between the presence or absence of saltwater disposals and the number of the earthquakes and to estimate the magnitude of the average causal effect. The second proposed workflow incorporates the temporal domain which holds more scientific interests. Finally, the analysis is repeated for different grid configurations to directly assess the sensitivity of statistical results.