We present a Transfer Causal Learning (TCL) framework when target and source domains share the same covariate/feature spaces whereas the treatment assignment mechanisms might differ, aiming to improve causal/treatment effect estimation accuracy in limited data. Limited data is very common in medical applications, where some rare medical conditions, such as sepsis, are of interest. Our proposed method, named \(\ell _1\) -TCL, incorporates \(\ell _1\) regularized transfer learning (TL) for nuisance models (e.g., propensity score model); the TL estimator of the nuisance parameters is plugged into downstream average treatment effect estimators (e.g., inverse probability-weighted estimator). We establish non-asymptotic recovery guarantees for the \(\ell _1\) -TCL with generalized linear model (GLM) under the sparsity assumption in the high-dimensional setting and demonstrate the empirical benefits of \(\ell _1\) -TCL through extensive numerical simulation for GLM and recent neural network nuisance models. Our method is subsequently extended to real data and generates meaningful insights consistent with medical literature, a case where all baseline methods fail.