Abstract Background Mendelian Randomization (MR) is a widely used tool to infer causal relationships. Yet, little research has been conducted on the elucidation of environment specific causal effects, despite mounting evidence for the relevance of causal effect modifying environmental variables. Methods To investigate potential modifications of causal effects, we extended two-stage-least-squares MR to investigate interaction effects (2SLS-I). We first tested 2SLS-I in a wide range of realistic simulation settings including quadratic and environment-dependent causal effects. Next, we applied 2SLS-I to investigate how environmental variables such as age, socioeconomic deprivation, and smoking modulate causal effects between a range of epidemiologically relevant exposure (such as systolic blood pressure, education, and body fat percentage) - outcome (e.g. forced expiratory volume (FEV1), CRP, and LDL cholesterol) pairs (in up to 337’392 individuals of the UK biobank). Results In simulations, 2SLS-I yielded unbiased interaction estimates, even in presence of non-linear causal effects. Applied to real data, 2SLS-I allowed for the detection of 182 interactions (P<0.001), with age, socioeconomic deprivation, and smoking being identified as important modifiers of many clinically relevant causal effects. For example, the positive causal effect of Triglycerides on systolic blood pressure was significantly attenuated in the elderly whilst the positive causal effect of Gamma-glutamyl transferase on CRP was intensified in smokers. Conclusion We present 2SLS-I, a method to simultaneously investigate environment-specific and non-linear causal effects. Our results highlight the importance of environmental variables in modifying well-established causal effects.