Triangulation is a method used in aetiological epidemiology to improve causal inference by integrating results from different approaches that have different and unrelated sources of potential bias. The aim of this paper is to illustrate how triangulation can be used to strengthen causal conclusions in aetiological epidemiology. We propose a minimum set of criteria for triangulation in aetiological epidemiology, including comparing results from at least two different approaches with differing and unrelated key sources of bias, ensuring the approaches address the same causal question, and considering the duration and timing of exposure when comparing results. We also emphasize the importance of explicitly acknowledging the key sources of bias and their expected direction in each approach, and seeking approaches that would bias the true causal effect in different directions. Triangulation is used in many research fields, including sociology, education, theoretical physics, and mathematics. In aetiological epidemiology, triangulation involves integrating evidence from different epidemiological approaches that have differing and unrelated key sources of bias. This approach aims to draw qualitative conclusions by exploiting differences in biases between approaches. The idea behind triangulation is that when we compare different approaches with assumed unrelated sources of bias, particularly if the expected direction of bias for some of the approaches is different, we would not expect to obtain the same estimates of the causal effect (unless all were unbiased). We propose that the following minimum set of criteria should be fulfilled for triangulation to be valid in aetiological epidemiology: (i) results from at least two, but ideally more, different approaches, with differing and unrelated key sources of potential biases, are compared; (ii) the different approaches address the same underlying causal question; (iii) related to (ii), for each approach the duration and timing of exposure that it assesses is taken into account when comparing results; (iv) for each approach, the key sources of bias are explicitly acknowledged when comparing results; (v) for each approach, the expected direction of all key sources of potential bias are made explicit where this is feasible, and ideally within the set of approaches being compared there are approaches with potential biases that are in opposite directions. Where results from two or more approaches fulfilling these criteria point to the same answer, this strengthens causal inference. Pointing to the same conclusion does not mean that the results are statistically consistent and could be pooled; currently triangulation will mostly provide a qualitative assessment of the strength of evidence regarding causality. Where results point to different causal answers, understanding the key sources of bias can help direct researchers to what further research is needed to answer the causal question.Triangulation is a method used in aetiological epidemiology to improve causal inference by integrating results from different approaches that have different and unrelated sources of potential bias. The aim of this paper is to illustrate how triangulation can be used to strengthen causal conclusions in aetiological epidemiology. We propose a minimum set of criteria for triangulation in aetiological epidemiology, including comparing results from at least two different approaches with differing and unrelated key sources of bias, ensuring the approaches address the same causal question, and considering the duration and timing of exposure when comparing results. We also emphasize the importance of explicitly acknowledging the key sources of bias and their expected direction in each approach, and seeking approaches that would bias the true causal effect in different directions. Triangulation is used in many research fields, including sociology, education, theoretical physics, and mathematics. In aetiological epidemiology, triangulation involves integrating evidence from different epidemiological approaches that have differing and unrelated key sources of bias. This approach aims to draw qualitative conclusions by exploiting differences in biases between approaches. The idea behind triangulation is that when we compare different approaches with assumed unrelated sources of bias, particularly if the expected direction of bias for some of the approaches is different, we would not expect to obtain the same estimates of the causal effect (unless all were unbiased). We propose that the following minimum set of criteria should be fulfilled for triangulation to be valid in aetiological epidemiology: (i) results from at least two, but ideally more, different approaches, with differing and unrelated key sources of potential biases, are compared; (ii) the different approaches address the same underlying causal question; (iii) related to (ii), for each approach the duration and timing of exposure that it assesses is taken into account when comparing results; (iv) for each approach, the key sources of bias are explicitly acknowledged when comparing results; (v) for each approach, the expected direction of all key sources of potential bias are made explicit where this is feasible, and ideally within the set of approaches being compared there are approaches with potential biases that are in opposite directions. Where results from two or more approaches fulfilling these criteria point to the same answer, this strengthens causal inference. Pointing to the same conclusion does not mean that the results are statistically consistent and could be pooled; currently triangulation will mostly provide a qualitative assessment of the strength of evidence regarding causality. Where results point to different causal answers, understanding the key sources of bias can help direct researchers to what further research is needed to answer the causal question.