TY - JOUR

T1 - Toward Causal Inference for Spatio-Temporal Data

T2 - Conflict and Forest Loss in Colombia

AU - Christiansen, Rune

AU - Baumann, Matthias

AU - Kuemmerle, Tobias

AU - Mahecha, Miguel D.

AU - Peters, Jonas Martin

PY - 2022

Y1 - 2022

N2 - How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates X on a real-valued response Y. We introduce a procedure for estimating causal effects and a nonparametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (P = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, P = 0.047) and negative effects (e.g., Magdalena, P = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online.

AB - How does armed conflict influence tropical forest loss? For Colombia, both enhancing and reducing effect estimates have been reported. However, a lack of causal methodology has prevented establishing clear causal links between these two variables. In this work, we propose a class of causal models for spatio-temporal stochastic processes which allows us to formally define and quantify the causal effect of a vector of covariates X on a real-valued response Y. We introduce a procedure for estimating causal effects and a nonparametric hypothesis test for these effects being zero. Our application is based on geospatial information on conflict events and remote-sensing-based data on forest loss between 2000 and 2018 in Colombia. Across the entire country, we estimate the effect to be slightly negative (conflict reduces forest loss) but insignificant (P = 0.578), while at the provincial level, we find both positive effects (e.g., La Guajira, P = 0.047) and negative effects (e.g., Magdalena, P = 0.004). The proposed methods do not make strong distributional assumptions, and allow for arbitrarily many latent confounders, given that these confounders do not vary across time. Our theoretical findings are supported by simulations, and code is available online.

U2 - 10.1080/01621459.2021.2013241

DO - 10.1080/01621459.2021.2013241

M3 - Journal article

VL - 117

SP - 591

EP - 601

JO - Journal of the American Statistical Association

JF - Journal of the American Statistical Association

SN - 0162-1459

ER -