Saltwater disposal has been identified as the dominant causal factor
that contribute to induced seismicity. Physical models rely on
mechanistic understanding to infer causality where they evaluate various
conditions for fault slips albeit with a high degree of uncertainty due
to sparse data and subsurface heterogeneity. Given these uncertainties,
statistical analysis is designed to measure statistical associations in
the observed data with parametric regression models and interpret the
significance of specific coefficient as evidence of causation. However,
it is often difficult to interrogate the coefficients between different
statistical models as the coefficients hold different implications. We
propose a causal inference framework with the potential outcomes
perspective to explicitly define what we meant by causal effect and
declare necessary assumptions to ensure consistency between models for
model comparison. The proposed workflow is applied to the Fort-Worth
Basin of North Central Texas with the area of interest is discretized
into non-overlapping grid blocks. 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. In addition, our
analysis is repeated for different grid configurations to directly
assess the sensitivity of statistical results. We have identified a
stable and statistically significant causal relationship between the
presence of saltwater disposals and the number of earthquakes and have
estimated there are, on average, 13 more earthquakes occurring in grids
with saltwater disposals.